Double_AE_Step-by-Step_PCA.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3"},"colab":{"name":"Double_AE_Step-by-Step_PCA.ipynb","provenance":[],"collapsed_sections":[]},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"id":"SE49EHPK4zIL","colab_type":"code","outputId":"5462e93d-c277-43a2-988f-1ee4350415b2","executionInfo":{"status":"error","timestamp":1589195523170,"user_tz":-330,"elapsed":3253,"user":{"displayName":"PRITAM PALIT","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Giix9Z-rAuOW8-Wch3FyW7hklDgx0X8ExfVyr5q5A=s64","userId":"02421712431687232718"}},"colab":{"base_uri":"https://localhost:8080/","height":132}},"source":["--------------------------------------------------------------------------------------------------------------import tensorflow as tf\n","device_name = tf.test.gpu_device_name()\n","if device_name != '/device:GPU:0':\n","  raise SystemError('GPU device not found')\n","print('Found GPU at: {}'.format(device_name))-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------"],"execution_count":1,"outputs":[{"output_type":"error","ename":"SyntaxError","evalue":"ignored","traceback":["\u001b[0;36m  File \u001b[0;32m\"<ipython-input-1-14f6146421ea>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    --------------------------------------------------------------------------------------------------------------import tensorflow as tf\u001b[0m\n\u001b[0m                                                                                                                       ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"]}]},{"cell_type":"code","metadata":{"id":"vvMT6I6BHHN2","colab_type":"code","outputId":"dde42695-fb32-40f8-8b74-04db629c705b","executionInfo":{"status":"ok","timestamp":1589159868201,"user_tz":-330,"elapsed":826743,"user":{"displayName":"PRITAM PALIT","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Giix9Z-rAuOW8-Wch3FyW7hklDgx0X8ExfVyr5q5A=s64","userId":"02421712431687232718"}},"colab":{"base_uri":"https://localhost:8080/","height":315}},"source":["------------------------------------------------------------import pandas as pd\n","#from google.colab import files\n","from google.colab import drive\n","drive.mount('/content/drive')\n","import matplotlib.pyplot as plt\n","import numpy as np\n","from ast import literal_eval\n","import json\n","#%matplotlib inline\n","\n","\n","#uploaded = files.upload()\n","#import io \n","#df= pd.read_csv(io.BytesIO(uploaded['Tot_ZeroBias_UL2017_DataFrame_chargeInner_PXLayer_1.csv']))\n","#df= pd.read_csv('ZeroBias_2017UL_DataFrame_ChargeInnerLayer4.txt')\n","df= pd.read_csv('/content/drive/My Drive/Colab Notebooks/Tot_ZeroBias_UL2017_DataFrame_chargeInner_PXLayer_1.csv')\n","df['histo']=df['histo'].apply(literal_eval)\n","\n","df.set_index(['fromrun','fromlumi'], inplace=True, drop=False)\n","df.sort_index(inplace=True)\n","print(df['histo'].shape)\n","print(df.tail())\n","print(df.shape)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n","\n","Enter your authorization code:\n","··········\n","Mounted at /content/drive\n","(225954,)\n","                  Unnamed: 0  ...                                              histo\n","fromrun fromlumi              ...                                                   \n","306462  95             67823  ...  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n","        96             67824  ...  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n","        97             67825  ...  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n","        98             67826  ...  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n","        99             67827  ...  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n","\n","[5 rows x 10 columns]\n","(225954, 10)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"AwqbcqC86TL2","colab_type":"code","colab":{}},"source":["import seaborn as sns\n","from pylab import rcParams\n","from keras.callbacks import ModelCheckpoint, TensorBoard\n","from keras import regularizers\n","from sklearn.preprocessing import StandardScaler\n","from sklearn.model_selection import train_test_split\n","from sklearn.metrics import confusion_matrix, precision_recall_curve\n","from sklearn.metrics import recall_score, classification_report, auc, roc_curve\n","from sklearn.metrics import precision_recall_fscore_support, f1_score\n","from numpy.random import seed\n","seed(1)\n","import tensorflow\n","#from tensorflow import set_random_seed\n","tensorflow.random.set_seed(2)\n","#set_random_seed(2)\n","SEED = 123 #used to help randomly select the data points\n","DATA_SPLIT_PCT = 0.2\n","DATA_SPLIT_PCT_VALID = 0.1\n","rcParams['figure.figsize'] = 10, 8\n","\n","\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"IF45uVsH846x","colab_type":"code","outputId":"8755f586-2cd9-46d9-8481-c51448424fe6","executionInfo":{"status":"ok","timestamp":1589161851668,"user_tz":-330,"elapsed":185537,"user":{"displayName":"PRITAM PALIT","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Giix9Z-rAuOW8-Wch3FyW7hklDgx0X8ExfVyr5q5A=s64","userId":"02421712431687232718"}},"colab":{"base_uri":"https://localhost:8080/","height":52}},"source":["#add Golden JSON labels to the DF\n","import json\n","\n","def checkLS(run,ls):\n","    isok=False\n","    global jsondata\n","\n","    if str(run) in jsondata.keys():\n","        for i in jsondata[str(run)]:\n","            if (ls>=i[0] and ls <=i[1]):\n","                isok=True\n","                return isok\n","        return   isok\n","\n","#load the golden json file\n","jsondata={}\n","with open('/content/drive/My Drive/Colab Notebooks/GoldenJSON17.json') as json_file:\n","    jsondata = json.load(json_file)\n","\n","df['labels']=False #initialize to false\n","\n","for run in df['fromrun'].unique():\n","    for ls in df['fromlumi'][run]:\n","        df['labels'][run][ls]=checkLS(run,ls)\n","\n","'''\n","def checkLSAnomaly(run,ls):\n","    isok_anomaly=True\n","    global jsondata\n","    \n","    if str(run) in jsondata.keys():\n","        for i in jsondata[str(run)]:\n","            if (ls>=i[0] and ls <=i[1]):\n","                isok_anomaly=False\n","                return isok_anomaly\n","        return isok_anomaly\n","\n","\n","df['anomaly']=True #initialize\n","for run in df['fromrun'].unique():\n","    for ls in df['fromlumi'][run]:  \n","        df['anomaly'][run][ls]=checkLSAnomaly(run,ls)\n","'''\n","#fname_bad = 'BAD_Tot_ZeroBias_UL2017_DataFrame_chargeInner_PXLayer_1.txt'\n","#fname_gold = 'GOLDEN_Tot_ZeroBias_UL2017_DataFrame_chargeInner_PXLayer_1.csv'\n","#df[df['labels']!=True].to_csv(fname_bad)\n","#df[df['labels']==True].to_csv(fname_gold)\n","\n","#print(df[df['labels']!=True]) #to check against the Golden JSON\n","print(df[df['labels']==False].shape)\n","print(df[df['labels']==True].shape)\n","#print(df[df['anomaly']==True].shape)\n","#print(df[df['anomaly']==False].shape)\n","#print(df[df['anomaly']==False])\n","#print(df[df['anomaly']==True])\n","#print(df.shape)\n","#print(df['labels'])\n","#print(df['anomaly'])\n","\n","df['type']=df['labels']\n","df['type']=df['type'].apply(lambda x:0 if x== True else 1)\n","df[df['type']==0]\n","\n","df['mse2']=\"\""],"execution_count":0,"outputs":[{"output_type":"stream","text":["(23655, 12)\n","(202299, 12)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"mLedH8KoP1Xw","colab_type":"code","colab":{}},"source":["df_train, df_test = train_test_split(df, test_size=DATA_SPLIT_PCT, random_state=SEED)\n","df_train, df_valid = train_test_split(df_train, test_size=DATA_SPLIT_PCT_VALID, random_state=SEED)\n","\n","X_train_true = np.stack(df_train['histo'][df_train['labels']==True].values, axis=0)\n","X_train_false = np.stack(df_train['histo'][df_train['labels']==False].values, axis=0)\n","\n","X_valid_true = np.stack(df_valid['histo'][df_valid['labels']==True].values, axis=0)\n","X_valid_false = np.stack(df_valid['histo'][df_valid['labels']==False].values, axis=0)\n","\n","\n","X_test_true = np.stack(df_test['histo'][df_test['labels']==True].values, axis=0)\n","X_test_false = np.stack(df_test['histo'][df_test['labels']==False].values, axis=0)\n","\n","from sklearn.preprocessing import normalize\n","\n","X_train_true_norm=normalize(X_train_true, norm='l1', axis=1) #normalise the sample, i.e the rows\n","X_train_false_norm=normalize(X_train_false, norm='l1', axis=1)\n","\n","X_valid_true_norm=normalize(X_valid_true, norm='l1', axis=1) #normalise the sample, i.e the rows\n","X_valid_false_norm=normalize(X_valid_false, norm='l1', axis=1)\n","\n","X_test_true_norm=normalize(X_test_true, norm='l1', axis=1) #normalise the sample, i.e the rows\n","X_test_false_norm=normalize(X_test_false, norm='l1', axis=1)\n","\n","\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"rJklxfxE_He8","colab_type":"code","outputId":"bbbea430-7a10-4d35-8d67-aad07019af85","executionInfo":{"status":"ok","timestamp":1589162339473,"user_tz":-330,"elapsed":823,"user":{"displayName":"PRITAM PALIT","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Giix9Z-rAuOW8-Wch3FyW7hklDgx0X8ExfVyr5q5A=s64","userId":"02421712431687232718"}},"colab":{"base_uri":"https://localhost:8080/","height":330}},"source":["#build the model\n","import math\n","from keras.callbacks import ModelCheckpoint, EarlyStopping\n","from keras.layers import Input, Dense\n","from keras.layers.advanced_activations import PReLU\n","from keras.models import Model\n","from keras import backend as K\n","import tensorflow as tf\n","from keras.models import load_model\n","\n","def mseTop10(y_true, y_pred):\n","    top_values, _ = tf.nn.top_k(K.square(y_pred - y_true), k=10, sorted=True)\n","    mean=K.mean(top_values, axis=-1)\n","    return mean\n","\n","nb_epoch = 100\n","batch_size = 500\n","input_dim = X_train_true_norm.shape[1] #num of predictor variables, \n","encoding_dim = 10\n","#hidden_dim = int(encoding_dim / 2)\n","hidden_dim = 3\n","learning_rate = 1e-3\n","\n","input_layer = Input(shape=(input_dim, ))\n","encoder = Dense(encoding_dim, activation=\"tanh\")(input_layer)  #, activity_regularizer=regularizers.l1(learning_rate))(input_layer)\n","#encoder = Dense(hidden_dim, activation=\"tanh\")(encoder)\n","decoder = Dense(hidden_dim, activation=\"tanh\")(encoder)\n","decoder = Dense(encoding_dim, activation=\"tanh\")(decoder)\n","decoder = Dense(input_dim, activation=\"tanh\")(decoder)\n","autoencoder = Model(inputs=input_layer, outputs=decoder)\n","autoencoder.summary()"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Model: \"model_2\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","input_2 (InputLayer)         (None, 100)               0         \n","_________________________________________________________________\n","dense_5 (Dense)              (None, 10)                1010      \n","_________________________________________________________________\n","dense_6 (Dense)              (None, 3)                 33        \n","_________________________________________________________________\n","dense_7 (Dense)              (None, 10)                40        \n","_________________________________________________________________\n","dense_8 (Dense)              (None, 100)               1100      \n","=================================================================\n","Total params: 2,183\n","Trainable params: 2,183\n","Non-trainable params: 0\n","_________________________________________________________________\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"-3x-HTLdCWaj","colab_type":"code","outputId":"242abf31-7023-445c-c0b8-46cec11dea08","executionInfo":{"status":"ok","timestamp":1589162545967,"user_tz":-330,"elapsed":202911,"user":{"displayName":"PRITAM PALIT","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Giix9Z-rAuOW8-Wch3FyW7hklDgx0X8ExfVyr5q5A=s64","userId":"02421712431687232718"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["autoencoder.compile(metrics=['accuracy'],\n","                    loss=mseTop10,\n","                    optimizer='adam')\n","cp = ModelCheckpoint(filepath=\"/content/drive/My Drive/Colab Notebooks/autoencoder_classifier.h5\",\n","                               save_best_only=True,\n","                               verbose=0)\n","tb = TensorBoard(log_dir='/content/drive/My Drive/Colab Notebooks/logs',\n","                histogram_freq=0,\n","                write_graph=True,\n","                write_images=True)\n","history = autoencoder.fit(X_train_true_norm, X_train_true_norm,\n","                    epochs=nb_epoch,\n","                    batch_size=batch_size,\n","                    shuffle=True,\n","                    validation_data=(X_valid_true_norm, X_valid_true_norm),\n","                    verbose=1,\n","                    callbacks=[cp, tb]).history"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Train on 145591 samples, validate on 16212 samples\n","Epoch 1/100\n","145591/145591 [==============================] - 2s 15us/step - loss: 1.5581e-04 - accuracy: 0.1263 - val_loss: 2.3018e-05 - val_accuracy: 0.2813\n","Epoch 2/100\n","145591/145591 [==============================] - 2s 14us/step - loss: 1.3110e-05 - accuracy: 0.3602 - val_loss: 5.5036e-06 - val_accuracy: 0.4156\n","Epoch 3/100\n","145591/145591 [==============================] - 2s 14us/step - loss: 5.0527e-06 - accuracy: 0.4351 - val_loss: 4.3586e-06 - val_accuracy: 0.4386\n","Epoch 4/100\n","145591/145591 [==============================] - 2s 14us/step - loss: 4.4805e-06 - accuracy: 0.4395 - val_loss: 3.9882e-06 - val_accuracy: 0.4385\n","Epoch 5/100\n","145591/145591 [==============================] - 2s 15us/step - loss: 4.0346e-06 - accuracy: 0.4349 - val_loss: 3.4380e-06 - val_accuracy: 0.4349\n","Epoch 6/100\n","145591/145591 [==============================] - 2s 14us/step - loss: 3.2960e-06 - accuracy: 0.4350 - val_loss: 2.6995e-06 - val_accuracy: 0.4405\n","Epoch 7/100\n","145591/145591 [==============================] - 2s 15us/step - loss: 2.7127e-06 - accuracy: 0.4429 - val_loss: 2.3936e-06 - val_accuracy: 0.4502\n","Epoch 8/100\n","145591/145591 [==============================] - 2s 14us/step - loss: 2.4517e-06 - accuracy: 0.4500 - val_loss: 2.2016e-06 - val_accuracy: 0.4502\n","Epoch 9/100\n","145591/145591 [==============================] - 2s 13us/step - loss: 2.3303e-06 - accuracy: 0.4567 - val_loss: 2.1282e-06 - val_accuracy: 0.4584\n","Epoch 10/100\n","145591/145591 [==============================] - 2s 13us/step - loss: 2.2669e-06 - accuracy: 0.4613 - val_loss: 2.1302e-06 - val_accuracy: 0.4604\n","Epoch 11/100\n","145591/145591 [==============================] - 2s 13us/step - loss: 2.2371e-06 - accuracy: 0.4644 - val_loss: 2.0895e-06 - val_accuracy: 0.4654\n","Epoch 12/100\n","145591/145591 [==============================] - 2s 14us/step - loss: 2.2216e-06 - accuracy: 0.4637 - val_loss: 2.0749e-06 - val_accuracy: 0.4687\n","Epoch 13/100\n","145591/145591 [==============================] - 2s 13us/step - loss: 2.2048e-06 - accuracy: 0.4650 - val_loss: 2.4005e-06 - val_accuracy: 0.4669\n","Epoch 14/100\n","145591/145591 [==============================] - 2s 13us/step - loss: 2.2094e-06 - accuracy: 0.4652 - val_loss: 2.0497e-06 - val_accuracy: 0.4655\n","Epoch 15/100\n","145591/145591 [==============================] - 2s 14us/step - loss: 2.1984e-06 - accuracy: 0.4655 - val_loss: 2.0836e-06 - val_accuracy: 0.4552\n","Epoch 16/100\n","145591/145591 [==============================] - 2s 15us/step - loss: 2.1916e-06 - accuracy: 0.4649 - val_loss: 2.0560e-06 - val_accuracy: 0.4627\n","Epoch 17/100\n","145591/145591 [==============================] - 2s 14us/step - loss: 2.1772e-06 - accuracy: 0.4653 - val_loss: 2.1238e-06 - val_accuracy: 0.4559\n","Epoch 18/100\n","145591/145591 [==============================] - 2s 14us/step - loss: 2.1743e-06 - accuracy: 0.4645 - val_loss: 2.0681e-06 - val_accuracy: 0.4655\n","Epoch 19/100\n","145591/145591 [==============================] - 2s 14us/step - loss: 2.1812e-06 - accuracy: 0.4639 - val_loss: 2.0374e-06 - val_accuracy: 0.4565\n","Epoch 20/100\n","145591/145591 [==============================] - 2s 14us/step - loss: 2.1774e-06 - accuracy: 0.4619 - val_loss: 2.1049e-06 - val_accuracy: 0.4425\n","Epoch 21/100\n","145591/145591 [==============================] - 2s 15us/step - loss: 2.1909e-06 - accuracy: 0.4600 - val_loss: 2.0677e-06 - val_accuracy: 0.4740\n","Epoch 22/100\n","145591/145591 [==============================] - 2s 15us/step - loss: 2.1815e-06 - accuracy: 0.4593 - val_loss: 2.0534e-06 - val_accuracy: 0.4529\n","Epoch 23/100\n","145591/145591 [==============================] - 2s 13us/step - loss: 2.2112e-06 - accuracy: 0.4610 - val_loss: 2.2623e-06 - val_accuracy: 0.4631\n","Epoch 24/100\n","145591/145591 [==============================] - 2s 14us/step - loss: 2.1669e-06 - accuracy: 0.4610 - val_loss: 2.0090e-06 - val_accuracy: 0.4497\n","Epoch 25/100\n","145591/145591 [==============================] - 2s 13us/step - loss: 2.1772e-06 - accuracy: 0.4588 - val_loss: 2.0479e-06 - val_accuracy: 0.4558\n","Epoch 26/100\n","145591/145591 [==============================] - 2s 15us/step - loss: 2.1694e-06 - accuracy: 0.4591 - val_loss: 2.0456e-06 - val_accuracy: 0.4585\n","Epoch 27/100\n","145591/145591 [==============================] - 2s 15us/step - loss: 2.1719e-06 - accuracy: 0.4582 - val_loss: 2.0395e-06 - val_accuracy: 0.4592\n","Epoch 28/100\n","145591/145591 [==============================] - 2s 14us/step - loss: 2.1488e-06 - accuracy: 0.4584 - val_loss: 2.0382e-06 - val_accuracy: 0.4567\n","Epoch 29/100\n","145591/145591 [==============================] - 2s 14us/step - loss: 2.1539e-06 - accuracy: 0.4579 - val_loss: 2.0672e-06 - val_accuracy: 0.4627\n","Epoch 30/100\n","145591/145591 [==============================] - 2s 15us/step - loss: 2.1493e-06 - accuracy: 0.4574 - val_loss: 2.2492e-06 - val_accuracy: 0.4688\n","Epoch 31/100\n","145591/145591 [==============================] - 2s 14us/step - loss: 2.1577e-06 - accuracy: 0.4557 - val_loss: 2.0343e-06 - val_accuracy: 0.4435\n","Epoch 32/100\n","145591/145591 [==============================] - 2s 13us/step - loss: 2.1522e-06 - accuracy: 0.4550 - val_loss: 2.0895e-06 - val_accuracy: 0.4481\n","Epoch 33/100\n","145591/145591 [==============================] - 2s 13us/step - loss: 2.1533e-06 - accuracy: 0.4556 - val_loss: 2.0869e-06 - val_accuracy: 0.4526\n","Epoch 34/100\n","145591/145591 [==============================] - 2s 14us/step - loss: 2.1397e-06 - accuracy: 0.4574 - val_loss: 2.0188e-06 - val_accuracy: 0.4575\n","Epoch 35/100\n","145591/145591 [==============================] - 2s 14us/step - loss: 2.1395e-06 - accuracy: 0.4576 - val_loss: 2.0307e-06 - val_accuracy: 0.4702\n","Epoch 36/100\n","145591/145591 [==============================] - 2s 14us/step - loss: 2.1440e-06 - accuracy: 0.4577 - val_loss: 2.0396e-06 - val_accuracy: 0.4347\n","Epoch 37/100\n","145591/145591 [==============================] - 2s 14us/step - loss: 2.1351e-06 - accuracy: 0.4557 - val_loss: 2.0077e-06 - val_accuracy: 0.4507\n","Epoch 38/100\n","145591/145591 [==============================] - 2s 13us/step - loss: 2.1509e-06 - accuracy: 0.4563 - val_loss: 1.9972e-06 - val_accuracy: 0.4768\n","Epoch 39/100\n","145591/145591 [==============================] - 2s 14us/step - loss: 2.1520e-06 - accuracy: 0.4559 - val_loss: 2.0231e-06 - val_accuracy: 0.4548\n","Epoch 40/100\n","145591/145591 [==============================] - 2s 13us/step - loss: 2.1296e-06 - accuracy: 0.4585 - val_loss: 2.0585e-06 - val_accuracy: 0.4592\n","Epoch 41/100\n","145591/145591 [==============================] - 2s 13us/step - loss: 2.1438e-06 - accuracy: 0.4567 - val_loss: 2.0091e-06 - val_accuracy: 0.4579\n","Epoch 42/100\n","145591/145591 [==============================] - 2s 13us/step - loss: 2.1316e-06 - accuracy: 0.4573 - val_loss: 2.0152e-06 - val_accuracy: 0.4443\n","Epoch 43/100\n","145591/145591 [==============================] - 2s 13us/step - loss: 2.1355e-06 - accuracy: 0.4571 - val_loss: 2.0397e-06 - val_accuracy: 0.4595\n","Epoch 44/100\n","145591/145591 [==============================] - 2s 14us/step - loss: 2.1393e-06 - accuracy: 0.4564 - val_loss: 2.0257e-06 - val_accuracy: 0.4452\n","Epoch 45/100\n","145591/145591 [==============================] - 2s 14us/step - loss: 2.1268e-06 - accuracy: 0.4555 - val_loss: 1.9955e-06 - val_accuracy: 0.4466\n","Epoch 46/100\n","145591/145591 [==============================] - 2s 13us/step - loss: 2.1708e-06 - accuracy: 0.4569 - val_loss: 2.0315e-06 - val_accuracy: 0.4429\n","Epoch 47/100\n","145591/145591 [==============================] - 2s 13us/step - loss: 2.1274e-06 - accuracy: 0.4569 - val_loss: 2.0106e-06 - val_accuracy: 0.4521\n","Epoch 48/100\n","145591/145591 [==============================] - 2s 13us/step - loss: 2.1300e-06 - accuracy: 0.4572 - val_loss: 2.0411e-06 - val_accuracy: 0.4558\n","Epoch 49/100\n","145591/145591 [==============================] - 2s 14us/step - loss: 2.1400e-06 - accuracy: 0.4554 - val_loss: 2.0299e-06 - val_accuracy: 0.4459\n","Epoch 50/100\n","145591/145591 [==============================] - 2s 13us/step - loss: 2.1333e-06 - accuracy: 0.4566 - val_loss: 2.0980e-06 - val_accuracy: 0.4155\n","Epoch 51/100\n","145591/145591 [==============================] - 2s 13us/step - loss: 2.1332e-06 - accuracy: 0.4558 - val_loss: 2.1157e-06 - val_accuracy: 0.4481\n","Epoch 52/100\n","145591/145591 [==============================] - 2s 13us/step - loss: 2.1652e-06 - accuracy: 0.4535 - val_loss: 2.0071e-06 - val_accuracy: 0.4367\n","Epoch 53/100\n","145591/145591 [==============================] - 2s 13us/step - loss: 2.1258e-06 - accuracy: 0.4562 - val_loss: 2.0242e-06 - val_accuracy: 0.4453\n","Epoch 54/100\n","145591/145591 [==============================] - 2s 14us/step - loss: 2.1427e-06 - accuracy: 0.4560 - val_loss: 2.0289e-06 - val_accuracy: 0.4495\n","Epoch 55/100\n","145591/145591 [==============================] - 2s 14us/step - loss: 2.1275e-06 - accuracy: 0.4585 - val_loss: 2.0499e-06 - val_accuracy: 0.4565\n","Epoch 56/100\n","145591/145591 [==============================] - 2s 13us/step - loss: 2.1255e-06 - accuracy: 0.4577 - val_loss: 2.1307e-06 - val_accuracy: 0.4523\n","Epoch 57/100\n","145591/145591 [==============================] - 2s 13us/step - loss: 2.1515e-06 - accuracy: 0.4569 - val_loss: 2.1249e-06 - val_accuracy: 0.4636\n","Epoch 58/100\n","145591/145591 [==============================] - 2s 13us/step - loss: 2.1374e-06 - accuracy: 0.4536 - val_loss: 2.0265e-06 - val_accuracy: 0.4596\n","Epoch 59/100\n","145591/145591 [==============================] - 2s 13us/step - loss: 2.1374e-06 - accuracy: 0.4575 - val_loss: 2.0283e-06 - val_accuracy: 0.4450\n","Epoch 60/100\n","145591/145591 [==============================] - 2s 13us/step - loss: 2.1369e-06 - accuracy: 0.4548 - val_loss: 2.0973e-06 - val_accuracy: 0.4426\n","Epoch 61/100\n","145591/145591 [==============================] - 2s 14us/step - loss: 2.1337e-06 - accuracy: 0.4555 - val_loss: 2.0129e-06 - val_accuracy: 0.4297\n","Epoch 62/100\n","145591/145591 [==============================] - 2s 13us/step - loss: 2.1328e-06 - accuracy: 0.4558 - val_loss: 2.0123e-06 - val_accuracy: 0.4495\n","Epoch 63/100\n","145591/145591 [==============================] - 2s 13us/step - loss: 2.1383e-06 - accuracy: 0.4543 - val_loss: 2.0347e-06 - val_accuracy: 0.4521\n","Epoch 64/100\n","145591/145591 [==============================] - 2s 13us/step - loss: 2.1222e-06 - accuracy: 0.4542 - val_loss: 2.0486e-06 - val_accuracy: 0.4600\n","Epoch 65/100\n","145591/145591 [==============================] - 2s 14us/step - loss: 2.1176e-06 - accuracy: 0.4567 - val_loss: 2.0409e-06 - val_accuracy: 0.4498\n","Epoch 66/100\n","145591/145591 [==============================] - 2s 13us/step - loss: 2.1236e-06 - accuracy: 0.4577 - val_loss: 2.0132e-06 - val_accuracy: 0.4443\n","Epoch 67/100\n","145591/145591 [==============================] - 2s 13us/step - loss: 2.1245e-06 - accuracy: 0.4565 - val_loss: 2.0342e-06 - val_accuracy: 0.4580\n","Epoch 68/100\n","145591/145591 [==============================] - 2s 13us/step - loss: 2.1236e-06 - accuracy: 0.4570 - val_loss: 2.0023e-06 - val_accuracy: 0.4600\n","Epoch 69/100\n","145591/145591 [==============================] - 2s 13us/step - loss: 2.1275e-06 - accuracy: 0.4546 - val_loss: 2.0131e-06 - val_accuracy: 0.4637\n","Epoch 70/100\n","145591/145591 [==============================] - 2s 14us/step - loss: 2.1293e-06 - accuracy: 0.4566 - val_loss: 2.0378e-06 - val_accuracy: 0.4394\n","Epoch 71/100\n","145591/145591 [==============================] - 2s 13us/step - loss: 2.1334e-06 - accuracy: 0.4544 - val_loss: 2.0007e-06 - val_accuracy: 0.4371\n","Epoch 72/100\n","145591/145591 [==============================] - 2s 14us/step - loss: 2.1168e-06 - accuracy: 0.4574 - val_loss: 2.0201e-06 - val_accuracy: 0.4426\n","Epoch 73/100\n","145591/145591 [==============================] - 2s 14us/step - loss: 2.1169e-06 - accuracy: 0.4574 - val_loss: 2.0149e-06 - val_accuracy: 0.4485\n","Epoch 74/100\n","145591/145591 [==============================] - 2s 14us/step - loss: 2.1214e-06 - accuracy: 0.4559 - val_loss: 2.0212e-06 - val_accuracy: 0.4453\n","Epoch 75/100\n","145591/145591 [==============================] - 2s 13us/step - loss: 2.1258e-06 - accuracy: 0.4563 - val_loss: 2.0402e-06 - val_accuracy: 0.4312\n","Epoch 76/100\n","145591/145591 [==============================] - 2s 13us/step - loss: 2.1368e-06 - accuracy: 0.4568 - val_loss: 2.0175e-06 - val_accuracy: 0.4569\n","Epoch 77/100\n","145591/145591 [==============================] - 2s 14us/step - loss: 2.1184e-06 - accuracy: 0.4555 - val_loss: 2.0320e-06 - val_accuracy: 0.4616\n","Epoch 78/100\n","145591/145591 [==============================] - 2s 15us/step - loss: 2.1227e-06 - accuracy: 0.4552 - val_loss: 2.0415e-06 - val_accuracy: 0.4645\n","Epoch 79/100\n","145591/145591 [==============================] - 2s 14us/step - loss: 2.1264e-06 - accuracy: 0.4569 - val_loss: 2.0007e-06 - val_accuracy: 0.4544\n","Epoch 80/100\n","145591/145591 [==============================] - 2s 13us/step - loss: 2.1375e-06 - accuracy: 0.4556 - val_loss: 1.9912e-06 - val_accuracy: 0.4614\n","Epoch 81/100\n","145591/145591 [==============================] - 2s 13us/step - loss: 2.1342e-06 - accuracy: 0.4559 - val_loss: 2.0030e-06 - val_accuracy: 0.4525\n","Epoch 82/100\n","145591/145591 [==============================] - 2s 13us/step - loss: 2.1286e-06 - accuracy: 0.4577 - val_loss: 1.9968e-06 - val_accuracy: 0.4636\n","Epoch 83/100\n","145591/145591 [==============================] - 2s 13us/step - loss: 2.1131e-06 - accuracy: 0.4556 - val_loss: 2.0028e-06 - val_accuracy: 0.4573\n","Epoch 84/100\n","145591/145591 [==============================] - 2s 13us/step - loss: 2.1435e-06 - accuracy: 0.4551 - val_loss: 2.0725e-06 - val_accuracy: 0.4589\n","Epoch 85/100\n","145591/145591 [==============================] - 2s 14us/step - loss: 2.1262e-06 - accuracy: 0.4562 - val_loss: 2.0739e-06 - val_accuracy: 0.4586\n","Epoch 86/100\n","145591/145591 [==============================] - 2s 14us/step - loss: 2.1242e-06 - accuracy: 0.4567 - val_loss: 2.0181e-06 - val_accuracy: 0.4471\n","Epoch 87/100\n","145591/145591 [==============================] - 2s 14us/step - loss: 2.1129e-06 - accuracy: 0.4565 - val_loss: 2.0490e-06 - val_accuracy: 0.4628\n","Epoch 88/100\n","145591/145591 [==============================] - 2s 14us/step - loss: 2.1342e-06 - accuracy: 0.4558 - val_loss: 2.0381e-06 - val_accuracy: 0.4628\n","Epoch 89/100\n","145591/145591 [==============================] - 2s 13us/step - loss: 2.1309e-06 - accuracy: 0.4553 - val_loss: 2.0130e-06 - val_accuracy: 0.4595\n","Epoch 90/100\n","145591/145591 [==============================] - 2s 13us/step - loss: 2.1130e-06 - accuracy: 0.4579 - val_loss: 2.0078e-06 - val_accuracy: 0.4545\n","Epoch 91/100\n","145591/145591 [==============================] - 2s 14us/step - loss: 2.1626e-06 - accuracy: 0.4548 - val_loss: 2.0124e-06 - val_accuracy: 0.4474\n","Epoch 92/100\n","145591/145591 [==============================] - 2s 14us/step - loss: 2.1161e-06 - accuracy: 0.4555 - val_loss: 2.0310e-06 - val_accuracy: 0.4620\n","Epoch 93/100\n","145591/145591 [==============================] - 2s 13us/step - loss: 2.1175e-06 - accuracy: 0.4554 - val_loss: 2.0287e-06 - val_accuracy: 0.4432\n","Epoch 94/100\n","145591/145591 [==============================] - 2s 13us/step - loss: 2.1112e-06 - accuracy: 0.4555 - val_loss: 1.9951e-06 - val_accuracy: 0.4523\n","Epoch 95/100\n","145591/145591 [==============================] - 2s 13us/step - loss: 2.1099e-06 - accuracy: 0.4574 - val_loss: 2.0263e-06 - val_accuracy: 0.4425\n","Epoch 96/100\n","145591/145591 [==============================] - 2s 14us/step - loss: 2.1188e-06 - accuracy: 0.4573 - val_loss: 2.0426e-06 - val_accuracy: 0.4499\n","Epoch 97/100\n","145591/145591 [==============================] - 2s 13us/step - loss: 2.1419e-06 - accuracy: 0.4557 - val_loss: 2.0660e-06 - val_accuracy: 0.4493\n","Epoch 98/100\n","145591/145591 [==============================] - 2s 14us/step - loss: 2.1252e-06 - accuracy: 0.4552 - val_loss: 2.0314e-06 - val_accuracy: 0.4481\n","Epoch 99/100\n","145591/145591 [==============================] - 2s 13us/step - loss: 2.1256e-06 - accuracy: 0.4540 - val_loss: 2.0200e-06 - val_accuracy: 0.4503\n","Epoch 100/100\n","145591/145591 [==============================] - 2s 14us/step - loss: 2.1281e-06 - accuracy: 0.4560 - val_loss: 2.1363e-06 - val_accuracy: 0.4465\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"EVHpt9H6H8yZ","colab_type":"code","colab":{}},"source":["print(history)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"TB5sxIBPFOOa","colab_type":"code","outputId":"372b5701-ce57-464b-dbb0-dac359b9e078","executionInfo":{"status":"ok","timestamp":1589162546601,"user_tz":-330,"elapsed":199745,"user":{"displayName":"PRITAM PALIT","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Giix9Z-rAuOW8-Wch3FyW7hklDgx0X8ExfVyr5q5A=s64","userId":"02421712431687232718"}},"colab":{"base_uri":"https://localhost:8080/","height":513}},"source":["#print(history)\n","\n","line_styles = [(0, ()), (0, (1, 3)), (0, (3, 2))]\n","\n","color_palette = {\"Indigo\": {\n","                    50: \"#E8EAF6\",\n","                    100: \"#C5CAE9\",\n","                    200: \"#9FA8DA\",\n","                    300: \"#7986CB\",\n","                    400: \"#5C6BC0\",\n","                    500: \"#3F51B5\",\n","                    600: \"#3949AB\",\n","                    700: \"#303F9F\",\n","                    800: \"#283593\",\n","                    900: \"#1A237E\"},\n","                 \"Teal\": {      \n","                    50: \"#E0F2F1\",\n","                    100: \"#B2DFDB\",\n","                    200: \"#80CBC4\",\n","                    300: \"#4DB6AC\",\n","                    400: \"#26A69A\",\n","                    500: \"#009688\",\n","                    600: \"#00897B\",\n","                    700: \"#00796B\",\n","                    800: \"#00695C\",\n","                    900: \"#004D40\"}\n","                }\n","\n","# \"Loss\"\n","plt.figure(1)\n","plt.plot(history['loss'])\n","plt.plot(history['val_loss'])\n","plt.title('model loss')\n","plt.ylabel('loss')\n","plt.xlabel('epoch')\n","plt.legend(['train', 'validation'], loc='upper left')\n","plt.yscale(\"log\")\n","#plt.xlim(0,100)\n","plt.show()\n","\n","#plot_loss(history, \"Original model loss\")"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 720x576 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"KIckNz1D3QRM","colab_type":"code","outputId":"c3de099e-d7e3-4219-b481-1b3e32156c64","executionInfo":{"status":"ok","timestamp":1589162552876,"user_tz":-330,"elapsed":203011,"user":{"displayName":"PRITAM PALIT","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Giix9Z-rAuOW8-Wch3FyW7hklDgx0X8ExfVyr5q5A=s64","userId":"02421712431687232718"}},"colab":{"base_uri":"https://localhost:8080/","height":335}},"source":["X_train_all = np.stack(df_train['histo'].values, axis=0)\n","X_train_all_norm=normalize(X_train_all, norm='l1', axis=1)\n","\n","train_x_predictions = autoencoder.predict(X_train_all_norm)\n","#mse = np.mean(np.power(df_valid_x_rescaled - valid_x_predictions, 2), axis=1)\n","mse_train=K.eval(mseTop10(X_train_all_norm, train_x_predictions))\n","#mse_train=np.mean(np.power(X_train_all_norm - train_x_predictions, 2), axis=1)\n","print(mse_train)\n","error_df_train = pd.DataFrame({'reconstruction_error': mse_train,\n","                        'true_class': df_train['type']})\n","error_df_train.describe()\n"],"execution_count":0,"outputs":[{"output_type":"stream","text":["[2.18414094e-06 8.30898892e-07 1.62234098e-06 ... 8.70230916e-07\n"," 6.31028440e-04 3.56193102e-06]\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["<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>reconstruction_error</th>\n","      <th>true_class</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>1.626860e+05</td>\n","      <td>162686.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>3.144703e-05</td>\n","      <td>0.105080</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>1.370228e-04</td>\n","      <td>0.306657</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>1.959578e-07</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>1.025253e-06</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>1.804215e-06</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>2.672257e-06</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>8.396471e-03</td>\n","      <td>1.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["       reconstruction_error     true_class\n","count          1.626860e+05  162686.000000\n","mean           3.144703e-05       0.105080\n","std            1.370228e-04       0.306657\n","min            1.959578e-07       0.000000\n","25%            1.025253e-06       0.000000\n","50%            1.804215e-06       0.000000\n","75%            2.672257e-06       0.000000\n","max            8.396471e-03       1.000000"]},"metadata":{"tags":[]},"execution_count":37}]},{"cell_type":"code","metadata":{"id":"M4gFznG3a2u_","colab_type":"code","outputId":"aaa94415-96f4-44fc-c8e2-1d6de1135e5a","executionInfo":{"status":"ok","timestamp":1589162553488,"user_tz":-330,"elapsed":200999,"user":{"displayName":"PRITAM PALIT","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Giix9Z-rAuOW8-Wch3FyW7hklDgx0X8ExfVyr5q5A=s64","userId":"02421712431687232718"}},"colab":{"base_uri":"https://localhost:8080/","height":335}},"source":["X_valid_all = np.stack(df_valid['histo'].values, axis=0)\n","X_valid_all_norm=normalize(X_valid_all, norm='l1', axis=1)\n","\n","valid_x_predictions = autoencoder.predict(X_valid_all_norm)\n","#mse = np.mean(np.power(df_valid_x_rescaled - valid_x_predictions, 2), axis=1)\n","mse_valid=K.eval(mseTop10(X_valid_all_norm, valid_x_predictions))\n","#mse_valid=np.mean(np.power(X_valid_all_norm - valid_x_predictions, 2), axis=1)\n","print(mse_valid)\n","error_df_valid = pd.DataFrame({'reconstruction_error': mse_valid,\n","                        'true_class': df_valid['type']})\n","error_df_valid.describe()\n","\n"],"execution_count":0,"outputs":[{"output_type":"stream","text":["[2.50667228e-06 6.33025018e-07 1.45593509e-06 ... 1.86020745e-06\n"," 1.47854168e-06 1.26064276e-06]\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["<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>reconstruction_error</th>\n","      <th>true_class</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>1.807700e+04</td>\n","      <td>18077.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>3.134746e-05</td>\n","      <td>0.103170</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>1.314342e-04</td>\n","      <td>0.304189</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>2.515561e-07</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>1.031448e-06</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>1.810357e-06</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>2.677766e-06</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>1.849503e-03</td>\n","      <td>1.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["       reconstruction_error    true_class\n","count          1.807700e+04  18077.000000\n","mean           3.134746e-05      0.103170\n","std            1.314342e-04      0.304189\n","min            2.515561e-07      0.000000\n","25%            1.031448e-06      0.000000\n","50%            1.810357e-06      0.000000\n","75%            2.677766e-06      0.000000\n","max            1.849503e-03      1.000000"]},"metadata":{"tags":[]},"execution_count":38}]},{"cell_type":"code","metadata":{"id":"SjWdWaV4kBcf","colab_type":"code","outputId":"baaf1298-d738-4424-e21b-c1dfc84c3f67","executionInfo":{"status":"ok","timestamp":1589162555538,"user_tz":-330,"elapsed":200520,"user":{"displayName":"PRITAM PALIT","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Giix9Z-rAuOW8-Wch3FyW7hklDgx0X8ExfVyr5q5A=s64","userId":"02421712431687232718"}},"colab":{"base_uri":"https://localhost:8080/","height":335}},"source":["X_test_all = np.stack(df_test['histo'].values, axis=0)\n","X_test_all_norm=normalize(X_test_all, norm='l1', axis=1)\n","\n","test_x_predictions = autoencoder.predict(X_test_all_norm)\n","#mse = np.mean(np.power(df_valid_x_rescaled - valid_x_predictions, 2), axis=1)\n","mse_test=K.eval(mseTop10(X_test_all_norm, test_x_predictions))\n","#mse_test=np.mean(np.power(X_test_all_norm - test_x_predictions, 2), axis=1)\n","print(mse_test)\n","error_df_test = pd.DataFrame({'reconstruction_error': mse_test,\n","                        'true_class': df_test['type']})\n","error_df_test.describe()\n"],"execution_count":0,"outputs":[{"output_type":"stream","text":["[4.48932022e-06 1.08572698e-06 7.03273826e-07 ... 4.68739537e-06\n"," 1.69086608e-06 3.30337722e-06]\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["<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>reconstruction_error</th>\n","      <th>true_class</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>4.519100e+04</td>\n","      <td>45191.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>3.108190e-05</td>\n","      <td>0.103892</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>1.340072e-04</td>\n","      <td>0.305124</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>2.359372e-07</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>1.027717e-06</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>1.811733e-06</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>2.677871e-06</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>3.982321e-03</td>\n","      <td>1.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["       reconstruction_error    true_class\n","count          4.519100e+04  45191.000000\n","mean           3.108190e-05      0.103892\n","std            1.340072e-04      0.305124\n","min            2.359372e-07      0.000000\n","25%            1.027717e-06      0.000000\n","50%            1.811733e-06      0.000000\n","75%            2.677871e-06      0.000000\n","max            3.982321e-03      1.000000"]},"metadata":{"tags":[]},"execution_count":39}]},{"cell_type":"code","metadata":{"id":"7PiKMj5ezt7P","colab_type":"code","outputId":"bac28088-23bb-4a42-88a5-eef4b962109a","executionInfo":{"status":"ok","timestamp":1589162572458,"user_tz":-330,"elapsed":214449,"user":{"displayName":"PRITAM PALIT","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Giix9Z-rAuOW8-Wch3FyW7hklDgx0X8ExfVyr5q5A=s64","userId":"02421712431687232718"}},"colab":{"base_uri":"https://localhost:8080/","height":335}},"source":["X_all = np.stack(df['histo'].values, axis=0)\n","X_all_good = np.stack(df['histo'][df['labels']==True].values, axis=0)\n","X_all_bad = np.stack(df['histo'][df['labels']==False].values, axis=0)\n","X_all_norm=normalize(X_all, norm='l1', axis=1)\n","X_all_good_norm=normalize(X_all_good, norm='l1', axis=1)\n","X_all_bad_norm=normalize(X_all_bad, norm='l1', axis=1)\n","\n","x_predictions = autoencoder.predict(X_all_norm)\n","x_good_predictions = autoencoder.predict(X_all_good_norm)\n","x_bad_predictions = autoencoder.predict(X_all_bad_norm)\n","#mse = np.mean(np.power(df_valid_x_rescaled - valid_x_predictions, 2), axis=1)\n","mse_all=K.eval(mseTop10(X_all_norm, x_predictions))\n","mse_good_all=K.eval(mseTop10(X_all_good_norm, x_good_predictions))\n","mse_bad_all=K.eval(mseTop10(X_all_bad_norm, x_bad_predictions))\n","#mse_test=np.mean(np.power(X_test_all_norm - test_x_predictions, 2), axis=1)\n","print(mse_all)\n","error_df_all = pd.DataFrame({'reconstruction_error': mse_all,\n","                        'true_class': df['type']})\n","error_df_all.describe()\n"],"execution_count":0,"outputs":[{"output_type":"stream","text":["[2.57260715e-06 1.58024907e-06 2.18302199e-06 ... 6.31028440e-04\n"," 6.31028440e-04 6.31028440e-04]\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["<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>reconstruction_error</th>\n","      <th>true_class</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>2.259540e+05</td>\n","      <td>225954.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>3.136603e-05</td>\n","      <td>0.104689</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>1.359839e-04</td>\n","      <td>0.306154</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>1.959578e-07</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>1.026410e-06</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>1.806423e-06</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>2.673715e-06</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>8.396471e-03</td>\n","      <td>1.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["       reconstruction_error     true_class\n","count          2.259540e+05  225954.000000\n","mean           3.136603e-05       0.104689\n","std            1.359839e-04       0.306154\n","min            1.959578e-07       0.000000\n","25%            1.026410e-06       0.000000\n","50%            1.806423e-06       0.000000\n","75%            2.673715e-06       0.000000\n","max            8.396471e-03       1.000000"]},"metadata":{"tags":[]},"execution_count":40}]},{"cell_type":"code","metadata":{"id":"vZpJaTzTd07A","colab_type":"code","outputId":"858b2a4b-ded2-48ff-c17c-3b189634cda2","executionInfo":{"status":"ok","timestamp":1589162575016,"user_tz":-330,"elapsed":200448,"user":{"displayName":"PRITAM PALIT","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Giix9Z-rAuOW8-Wch3FyW7hklDgx0X8ExfVyr5q5A=s64","userId":"02421712431687232718"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["\n","rcParams['figure.figsize'] = 14, 6\n","'''\n","# reconstruction error for the normal transactions in the testing dataset\n","fig = plt.figure(1)\n","ax = fig.add_subplot(111)\n","Anomaly_error_df_valid = error_df_valid[(error_df_valid['true_class']== True)]\n","Golden_error_df_valid = error_df_valid[(error_df_valid['true_class']== False)]\n","ax.hist(Golden_error_df_valid.reconstruction_error.values, bins=1000, range=(0., 0.0002), color = 'yellow', ls='dashed',  alpha = 1.0, lw=3, log=True)\n","ax.hist(Anomaly_error_df_valid.reconstruction_error.values, bins=1000, range=(0., 0.0002), color = 'red', ls='dashed',  alpha = 0.3, lw=3, log=True)\n","#ax.set_alpha(0.9)\n","plt.show()\n","'''\n","# reconstruction error for the normal transactions in the testing dataset\n","#rcParams['figure.figsize'] = 10, 8\n","Anomaly_error_df_train = error_df_train[(error_df_train['true_class']== True)]\n","Golden_error_df_train = error_df_train[(error_df_train['true_class']== False)]\n","Anomaly_error_df_valid = error_df_valid[(error_df_valid['true_class']== True)]\n","Golden_error_df_valid = error_df_valid[(error_df_valid['true_class']== False)]\n","Anomaly_error_df_test = error_df_test[(error_df_test['true_class']== True)]\n","Golden_error_df_test = error_df_test[(error_df_test['true_class']== False)]\n","fig = plt.figure(1)\n","#ax = fig.add_subplot(131)\n","ax = fig.add_subplot()\n","ax.hist(Golden_error_df_train.reconstruction_error.values, bins=100, range=(0., 0.0010), color = 'yellow', label= 'Train : Golden', ls='dashed',  alpha = 1.0, lw=3, log=True)\n","ax.hist(Anomaly_error_df_train.reconstruction_error.values, bins=100, range=(0., 0.0010), color = 'red', label= 'Train : Bad', ls='dashed',  alpha = 0.3, lw=3, log=True)\n","ax.set_xlabel('Reconstruction_error for Train dataset')\n","plt.legend(['Golden', 'Bad'], loc='upper right')\n","fig = plt.figure(2)\n","#ax = fig.add_subplot(132)\n","ax = fig.add_subplot()\n","ax.hist(Golden_error_df_valid.reconstruction_error.values, bins=100, range=(0., 0.0010), color = 'yellow', label= 'validation : Golden', ls='dashed',  alpha = 1.0, lw=3, log=True)\n","ax.hist(Anomaly_error_df_valid.reconstruction_error.values, bins=100, range=(0., 0.0010), color = 'red', label= 'Validation : Bad', ls='dashed',  alpha = 0.3, lw=3, log=True)\n","ax.set_xlabel('Reconstruction_error for Validation dataset')\n","plt.legend(['Golden', 'Bad'], loc='upper right')\n","fig = plt.figure(3)\n","#ax = fig.add_subplot(133)\n","ax = fig.add_subplot()\n","ax.hist(Golden_error_df_test.reconstruction_error.values, bins=100, range=(0., 0.0010), color = 'yellow', label= 'Test : Golden', ls='dashed',  alpha = 1.0, lw=3, log=True)\n","ax.hist(Anomaly_error_df_test.reconstruction_error.values, bins=100, range=(0., 0.0010), color = 'red', label= 'Test : Bad', ls='dashed',  alpha = 0.3, lw=3, log=True)\n","ax.set_xlabel('Reconstruction_error for Test dataset')\n","#ax.set_alpha(0.9)\n","#plt.tight_layout()\n","plt.legend(['Golden', 'Bad'], loc='upper right')\n","plt.show()\n","\n"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure 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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 1008x432 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"t-tO-y8inrBS","colab_type":"code","outputId":"2bb605ad-0fa5-4816-d9c0-42d65fd96435","executionInfo":{"status":"ok","timestamp":1589162577152,"user_tz":-330,"elapsed":2096,"user":{"displayName":"PRITAM PALIT","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Giix9Z-rAuOW8-Wch3FyW7hklDgx0X8ExfVyr5q5A=s64","userId":"02421712431687232718"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["rcParams['figure.figsize'] = 10, 6\n","#ROC for Train\n","from numpy import sqrt\n","from numpy import argmax\n","from sklearn.metrics import (confusion_matrix, precision_recall_curve, auc,\n","                             roc_curve, recall_score, classification_report, f1_score,\n","                             precision_recall_fscore_support)\n","fpr, tpr, thresholds = roc_curve(error_df_train.true_class, error_df_train.reconstruction_error)\n","print(fpr)\n","print(tpr)\n","print(thresholds)\n","roc_auc = auc(fpr, tpr)\n","# calculate the g-mean for each threshold\n","gmeans = sqrt(tpr * (1-fpr))\n","# locate the index of the largest g-mean\n","ix = argmax(gmeans)\n","print('Best Threshold=%f, G-Mean=%.3f' % (thresholds[ix], gmeans[ix]))\n","plt.figure(1)\n","#plt.subplot(1,3,1)\n","plt.title('Receiver Operating Characteristic for Train')\n","plt.plot(fpr, tpr, label='AUC = %0.4f'% roc_auc)\n","plt.legend(loc='lower right')\n","plt.plot([0,1],[0,1],'r--')\n","plt.scatter(fpr[ix], tpr[ix], marker='o', color='black', label='Best')\n","plt.xlim([-0.001, 1])\n","plt.ylim([0, 1.001])\n","plt.ylabel('True Positive Rate')\n","plt.xlabel('False Positive Rate')\n","#plt.show();\n","\n","\n","#ROC for Validation\n","from numpy import sqrt\n","from numpy import argmax\n","from sklearn.metrics import (confusion_matrix, precision_recall_curve, auc,\n","                             roc_curve, recall_score, classification_report, f1_score,\n","                             precision_recall_fscore_support)\n","fpr, tpr, thresholds = roc_curve(error_df_valid.true_class, error_df_valid.reconstruction_error)\n","print(fpr)\n","print(tpr)\n","print(thresholds)\n","roc_auc = auc(fpr, tpr)\n","# calculate the g-mean for each threshold\n","gmeans = sqrt(tpr * (1-fpr))\n","# locate the index of the largest g-mean\n","ix = argmax(gmeans)\n","print('Best Threshold=%f, G-Mean=%.3f' % (thresholds[ix], gmeans[ix]))\n","plt.figure(2)\n","#plt.subplot(1,3,2)\n","plt.title('Receiver Operating Characteristic for Validation')\n","plt.plot(fpr, tpr, label='AUC = %0.4f'% roc_auc)\n","plt.legend(loc='lower right')\n","plt.plot([0,1],[0,1],'r--')\n","plt.scatter(fpr[ix], tpr[ix], marker='o', color='black', label='Best')\n","plt.xlim([-0.001, 1])\n","plt.ylim([0, 1.001])\n","plt.ylabel('True Positive Rate')\n","plt.xlabel('False Positive Rate')\n","#plt.show();\n","\n","# ROC for test\n","from numpy import sqrt\n","from numpy import argmax\n","from sklearn.metrics import (confusion_matrix, precision_recall_curve, auc,\n","                             roc_curve, recall_score, classification_report, f1_score,\n","                             precision_recall_fscore_support)\n","fpr, tpr, thresholds = roc_curve(error_df_test.true_class, error_df_test.reconstruction_error)\n","print(fpr)\n","print(tpr)\n","print(thresholds)\n","roc_auc = auc(fpr, tpr)\n","# calculate the g-mean for each threshold\n","gmeans = sqrt(tpr * (1-fpr))\n","# locate the index of the largest g-mean\n","ix = argmax(gmeans)\n","print('Best Threshold=%f, G-Mean=%.3f' % (thresholds[ix], gmeans[ix]))\n","fig = plt.figure(3)\n","#plt.subplot(1,3,3)\n","plt.title('Receiver Operating Characteristic for Test')\n","plt.plot(fpr, tpr, label='AUC = %0.4f'% roc_auc)\n","plt.legend(loc='lower right')\n","plt.plot([0,1],[0,1],'r--')\n","plt.scatter(fpr[ix], tpr[ix], marker='o', color='black', label='Best')\n","plt.xlim([-0.001, 1])\n","plt.ylim([0, 1.001])\n","plt.ylabel('True Positive Rate')\n","plt.xlabel('False Positive Rate')\n","plt.show();"],"execution_count":0,"outputs":[{"output_type":"stream","text":["[0.00000000e+00 6.86855644e-06 6.86855644e-06 ... 9.98571340e-01\n"," 9.98571340e-01 1.00000000e+00]\n","[0.         0.         0.00269085 ... 0.9999415  1.         1.        ]\n","[1.00839647e+00 8.39647128e-03 9.39568347e-04 ... 3.13238856e-07\n"," 3.13209094e-07 1.95957775e-07]\n","Best Threshold=0.000004, G-Mean=0.760\n","[0.         0.         0.         ... 0.99796447 0.99796447 1.        ]\n","[0.00000000e+00 5.36193029e-04 2.68096515e-03 ... 9.99463807e-01\n"," 1.00000000e+00 1.00000000e+00]\n","[1.00184950e+00 1.84950339e-03 8.18335387e-04 ... 3.34815862e-07\n"," 3.33073542e-07 2.51556149e-07]\n","Best Threshold=0.000004, G-Mean=0.768\n","[0.         0.         0.         ... 0.99659226 0.99659226 1.        ]\n","[0.00000000e+00 2.12992545e-04 1.27795527e-03 ... 9.99787007e-01\n"," 1.00000000e+00 1.00000000e+00]\n","[1.00398232e+00 3.98232109e-03 9.95211386e-04 ... 3.39571505e-07\n"," 3.39285943e-07 2.35937217e-07]\n","Best Threshold=0.000004, G-Mean=0.758\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 720x432 with 1 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\n","text/plain":["<Figure size 720x432 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"UWyY34qxoGvP","colab_type":"code","outputId":"0571f615-939a-4ca7-a07c-cc5eca9730d3","executionInfo":{"status":"ok","timestamp":1589162579943,"user_tz":-330,"elapsed":4855,"user":{"displayName":"PRITAM PALIT","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Giix9Z-rAuOW8-Wch3FyW7hklDgx0X8ExfVyr5q5A=s64","userId":"02421712431687232718"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["rcParams['figure.figsize'] = 10, 6\n","from numpy import argmax\n","#P-R curve for train\n","precision, recall, th = precision_recall_curve(error_df_train.true_class, error_df_train.reconstruction_error)\n","# convert to f score\n","fscore = (2 * precision * recall) / (precision + recall)\n","# locate the index of the largest f score\n","#ix = argmax(fscore)\n","#print('Best Threshold=%f, F-Score=%.3f' % (thresholds[ix], fscore[ix]))\n","plt.figure(1)\n","#plt.subplot(2,3,1)\n","plt.plot(recall, precision, 'b', label='Precision-Recall curve')\n","#plt.plot([0,1], [no_skill,no_skill], linestyle='--')\n","#plt.scatter(recall[ix], precision[ix], marker='o', color='black', label='Best')\n","plt.title('Recall vs Precision for Train')\n","plt.xlabel('Recall')\n","plt.ylabel('Precision')\n","plt.figure(2)\n","#plt.subplot(2,3,4)\n","plt.plot(th, precision[1:], label=\"Precision\",linewidth=5)\n","plt.plot(th, recall[1:], label=\"Recall\",linewidth=5)\n","plt.title('Precision and recall for different threshold values for Train')\n","plt.xlabel('Threshold')\n","plt.ylabel('Precision/Recall')\n","plt.legend()\n","#plt.show()\n","\n","\n","\n","#P-R curve for validation\n","precision, recall, th = precision_recall_curve(error_df_valid.true_class, error_df_valid.reconstruction_error)\n","# convert to f score\n","fscore = (2 * precision * recall) / (precision + recall)\n","# locate the index of the largest f score\n","#ix = argmax(fscore)\n","#print('Best Threshold=%f, F-Score=%.3f' % (thresholds[ix], fscore[ix]))\n","plt.figure(3)\n","#plt.subplot(2,3,2)\n","plt.plot(recall, precision, 'b', label='Precision-Recall curve')\n","#plt.plot([0,1], [no_skill,no_skill], linestyle='--')\n","#plt.scatter(recall[ix], precision[ix], marker='o', color='black', label='Best')\n","plt.title('Recall vs Precision for Validation')\n","plt.xlabel('Recall')\n","plt.ylabel('Precision')\n","plt.figure(4)\n","#plt.subplot(2,3,5)\n","plt.plot(th, precision[1:], label=\"Precision\",linewidth=5)\n","plt.plot(th, recall[1:], label=\"Recall\",linewidth=5)\n","plt.title('Precision and recall for different threshold values for Validation')\n","plt.xlabel('Threshold')\n","plt.ylabel('Precision/Recall')\n","plt.legend()\n","#plt.show()\n","\n","\n","#P-R curve for test\n","precision, recall, th = precision_recall_curve(error_df_test.true_class, error_df_test.reconstruction_error)\n","# convert to f score\n","fscore = (2 * precision * recall) / (precision + recall)\n","# locate the index of the largest f1 score\n","#---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n","#----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ix = argmax(fscore)\n","ix = argmax(fscore)\n","print('Best Threshold=%f, F-Score=%.3f' % (th[ix], fscore[ix]))\n","plt.figure(5)\n","#plt.subplot(2,3,3)\n","plt.plot(recall, precision, 'b', label='Precision-Recall curve')\n","#plt.plot([0,1], [no_skill,no_skill], linestyle='--')\n","plt.scatter(recall[ix], precision[ix], marker='o', color='black', label='Best')\n","plt.title('Recall vs Precision for Test')\n","plt.xlabel('Recall')\n","plt.ylabel('Precision')\n","plt.figure(6)\n","#plt.subplot(2,3,6)\n","plt.plot(th, precision[1:], label=\"Precision\",linewidth=5)\n","plt.plot(th, recall[1:], label=\"Recall\",linewidth=5)\n","plt.title('Precision and recall for different threshold values for Test')\n","plt.xlabel('Threshold')\n","plt.ylabel('Precision/Recall')\n","plt.legend()\n","plt.show()"],"execution_count":0,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:6: RuntimeWarning: invalid value encountered in true_divide\n","  \n"],"name":"stderr"},{"output_type":"stream","text":["Best Threshold=0.000013, F-Score=0.709\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure 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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 720x432 with 1 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\n","text/plain":["<Figure 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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 720x432 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"k1P7vTwJo5Tz","colab_type":"code","outputId":"3f3cba87-5db3-43f2-849b-ce79b37f5745","executionInfo":{"status":"ok","timestamp":1589162579945,"user_tz":-330,"elapsed":4826,"user":{"displayName":"PRITAM PALIT","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Giix9Z-rAuOW8-Wch3FyW7hklDgx0X8ExfVyr5q5A=s64","userId":"02421712431687232718"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["rcParams['figure.figsize'] = 14, 10\n","#confusion matrix for train\n","import seaborn as sns\n","threshold_train = 0.000014\n","LABELS = ['Good', 'Anomaly']\n","y_pred = [1 if e > threshold_train else 0 for e in error_df_train.reconstruction_error.values]\n","conf_matrix = confusion_matrix(error_df_train.true_class, y_pred)\n","plt.figure(1)\n","#plt.subplot(1,3,1)\n","#plt.figure(figsize=(12, 12))\n","sns.heatmap(conf_matrix, xticklabels=LABELS, yticklabels=LABELS, annot=True, fmt=\"d\");\n","plt.title(\"Confusion matrix for Train, th = 0.000014\")\n","plt.ylabel('True class')\n","plt.xlabel('Predicted class')\n","#plt.show()\n","\n","#confusion matrix for validation\n","#import seaborn as sns\n","threshold_valid = 0.000010\n","LABELS = ['Good', 'Anomaly']\n","y_pred = [1 if e > threshold_valid else 0 for e in error_df_valid.reconstruction_error.values]\n","conf_matrix = confusion_matrix(error_df_valid.true_class, y_pred)\n","#plt.figure(figsize=(12, 12))\n","#plt.subplot(1,3,2)\n","plt.figure(2)\n","sns.heatmap(conf_matrix, xticklabels=LABELS, yticklabels=LABELS, annot=True, fmt=\"d\");\n","plt.title(\"Confusion matrix for Validation, th = 0.000014\")\n","plt.ylabel('True class')\n","plt.xlabel('Predicted class')\n","#plt.show()\n","\n","#confusion matrix for test\n","#import seaborn as sns\n","threshold = 0.000013\n","LABELS = ['Good', 'Anomaly']\n","y_pred = [1 if e > threshold else 0 for e in error_df_test.reconstruction_error.values]\n","conf_matrix = confusion_matrix(error_df_test.true_class, y_pred)\n","#plt.figure(figsize=(12, 12))\n","plt.figure(3)\n","#plt.subplot(1,3,3)\n","sns.heatmap(conf_matrix, xticklabels=LABELS, yticklabels=LABELS, annot=True, fmt=\"d\");\n","plt.title(\"Confusion matrix for Test, th = 0.000013\")\n","plt.ylabel('True class')\n","plt.xlabel('Predicted class')\n","plt.show()"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure 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\n","text/plain":["<Figure 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pgcSJIkSW2WDa+U1SFlyV4O2R0YX467EVgmIlYGdgauzMznSoPgSmBE2bZUZt6YmQmMB0ZV1cvGgSRJkuohu9q2RMSYiJjQtIyZtToRMSgi7gAm07jBv6ls+kHpOnR0RCxcylYFHms6fFIp66180mzKe2W3IkmSJKmfZeZYYGzFPjOATSNiGeC3EbExcAjwJDC0HH8wcHiLqzuTyYEkSZLqoaurfcscyMwXgGuAEZn5ROk69BpwKrBl2e1xYPWmw1YrZb2Vrzab8l7ZOJAkSZLaLCKGl8SAiFgU+CDw1zJWgDKz0CjgnnLIhcC+ZdairYEXM/MJ4HJgp4hYNiKWBXYCLi/bXoqIrcu59gUuqKqX3YokSZJUD501W9HKwLiIGETjB/tzMvPiiLg6IoYDAdwBfLHsfymwCzAReBX4DEBmPhcR3wduKfsdnpnPldf7A6cBi9KYpajXmYrAxoEkSZLUdpl5F/DO2ZTv0MP+CRzQw7ZTgFNmUz4B2HhO6mXjQJIkSfXgE5IrOeZAkiRJEmByIEmSpLrorDEHHcnkQJIkSRJg40CSJElSYbciSZIk1YMDkiuZHEiSJEkCTA4kSZJUFw5IrmRyIEmSJAkwOZAkSVJdOOagksmBJEmSJMDkQJIkSXXhmINKJgeSJEmSAJMDSZIk1YXJQSWTA0mSJEmAyYEkSZLqInOga9DxTA4kSZIkASYHkiRJqgvHHFQyOZAkSZIEmBxIkiSpLkwOKpkcSJIkSQJMDiRJklQXaXJQxeRAkiRJEmDjQJIkSVJhtyJJkiTVgwOSK5kcSJIkSQJMDiRJklQXmQNdg45nciBJkiQJMDmQJElSXTjmoJLJgSRJkiTA5ECSJEl1YXJQyeRAkiRJEmByIEmSpLpIk4MqJgeSJEmSAJMDSZIk1UR2+ZyDKiYHkiRJkgCTA0mSJNWFsxVVMjmQJEmSBJgcSJIkqS6craiSyYEkSZIkwMaBJEmSpMJuRZIkSaoHpzKtZHIgSZIkCTA5kCRJUl04lWklkwNJkiRJgMmBJEmS6sLkoJLJgSRJkiTA5ECSJEl1kc5WVMXkQJIkSRJgciBJkqS6cMxBJZMDSZIkSYDJgSRJkurCJyRXMjmQJEmSBNg40AJoxowZfOzTB7D/Nw8FYNI/n2TPz3+VkZ/4LN/47//HtGnT3rD/ldfcwMbvGck99z84s+yk8Wcz8hOfZbc9Psefbrp1Zvl3f3gU2+26B6P2/mJ73owkzaEvH7gfd9x+FXfecTUHfflzAPzPf3+dRx+ewIRbrmDCLVcwcsQOA1xLaYBkV/uW+ZSNAy1wzvjNBayz1hoz148+4RT2+eQoLjvnFJZacgnOu/jymdumTHmVM35zAe/Y8K0zy/7+8KNcdtUfueCMX/CLo47g+//3M2bMmAHAqF0+yC+OOqJ9b0aS5sBGG72V/fb7FO/eZlc22/yD7LrLB3jLW9YC4JhjT2KLd+3EFu/aict+f/XAVlRSx7JxoAXKk5Of5ro/38x/fGhnADKTm269k5223xaA3Xf5AFdf95eZ+x930ng+u/fHGbrw0JllV19/IyN3fB9Dhw5ltVVWYo3VVuHukipssenbWXqpJdv4jiSp7zbYYD1uvvl2/vWvfzNjxgyuu/5GPjJq5EBXS+ocXdm+ZT7VksZBRGzW29KKa0oARx5zIl/ffz8iGn/aL7z4EksusTiDBw8CYMXhw5j89LMA3PfARJ6c/Azv22bLN5xj8tPPstKKw2eur7jCMCY//Uyb3oEkzb177/0r733vViy33LIsuugijByxA6uttgoA+3/pM9x265WcNPYnLLPM0gNcU0mdqlXJwU/KcjxwEzAWOKm8Pr6ngyJiTERMiIgJvxx/ZouqpgXVtX+6ieWWXYaNNlivct+uri5+fNxYvvnlz7ehZpLUHn/960T+93+P57JLf82lF/+KO+68lxkzuvjFieNZf4Nt2HyLnXjyycn874//Z6CrKqlDtWQq08x8P0BEnA9slpl3l/WNgcN6OW4sjYYE0555aP7NYzQgbr/rPq694Uau/8stvDZ1GlOmvMqPfvoLXn5lCtOnz2Dw4EE89fQzrDB8eaa8+i8mPvQonznwWwA889zzfPng73HckYeywvDlefKpp2ee96nJz7DC8GED9bYkaY6cetpZnHraWQAc8f1vM2nSE0ye/Hr6+cuTf8UFvxs3UNWTBlR20EPQImIR4DpgYRr35Odm5qERsTZwFrA8cCuwT2ZOjYiFgfHA5sCzwCcz85FyrkOA/YAZwEGZeXkpHwEcAwwCfpmZP6qqV6vHHLy1u2EAkJn3AG9r8TVVU1/70me46ndncMV54/jf732bLTffhCMPO5gtN3sHV1x7PQAXXPoHdtj23Sy5xOLccOnZXHHeOK44bxzv2GgDjjvyUDZ+2/q8/71bc9lVf2Tq1KlM+ueT/GPSP3n729Yf4HcnSX0zfPjyAKy++iqMGjWSM8/6LSuttMLM7aN2H8m99z4wUNWT9LrXgB0ycxNgU2BERGwNHAkcnZnrAs/TuOmn/Pt8KT+67EdEbAjsAWwEjAB+HhGDImIQjR47I4ENgT3Lvr1q9UPQ7oqIXwJnlPW9gLtafE3pDb72pc/yzUN/xHFjx/O29d/CR3fbqdf9111nTXbeYVs+vNcXGDxoEP/19f0ZNKgxZuGbh/6IW26/ixdeeIkdR+3N/vvtM3PwsyR1gt+cfRLLLb8s06ZN56CD/osXX3yJY356BJtssiGZyaOPTuJL+x880NWUBkYHDRTOzAReKatDypLADsCnSvk4Gr1uTgB25/UeOOcCP4uIKOVnZeZrwMMRMRHoHlA5MTMfAoiIs8q+9/VWr2jUqzVKXPIlYLtSdB1wQmb+u+pYuxVJWlAtusq2A10FSWqZ6VMfj4GuQ0+m/GDftt1fLvHd078AjGkqGlu60M9Uft2/FViXxq/8/wvcWNIBImJ14LLM3Dgi7gFGZOaksu3vwFY0Ggw3ZuYZpfxk4LJyiRGZ+blSvg+wVWYe2Fu9W5ocZOa/I+J44A80WkIPZOa0isMkSZKk/tfGh5M1j6XtZZ8ZwKYRsQzwW2CDdtStNy1tHETE9jTikEeAAFaPiNGZeV0rrytJkiTNLzLzhYi4Bng3sExEDM7M6cBqwONlt8eB1YFJETEYWJrGwOTu8m7Nx/RU3qNWD0j+CbBTZr4vM7cDdqYxgEKSJElqrw56CFpEDC+JARGxKPBB4H7gGuBjZbfRwAXl9YVlnbL96jJu4UJgj4hYuMx0tB5wM3ALsF5ErB0RQ2kMWr6wql6tHpA8JDNnTomQmQ9GxJAWX1OSJEnqdCsD48q4g4WAczLz4oi4DzgrIo4AbgdOLvufDJxeBhw/R+Nmn8y8NyLOoTHQeDpwQOmuREQcCFxOYyrTUzLz3qpKtXpA8ilAF2+crWhQZn626lgHJEtaUDkgWdKCrKMHJB+2Z9vuLxc/7MyO/Rx60+rk4EvAAcBBZf164OctvqYkSZKkudDq2Ypei4ifAVfibEWSJEkaSB30nINO5WxFkiRJkoDWdyvqnq3oAYCIWB84E9i8xdeVJEmS3qiNzzmYX7V6KtM3zVZE49HQkiRJkjpMq5ODCRHxS16frWhvYEKLrylJkiS9mWMOKrVrtqIvl3VnK5IkSZI6VEu6FUXE7hFxQGa+lplH0Xh082Y0pjT9cCuuKUmSJGnetCo5+BblqW3FUBqDkJcATgXObdF1JUmSpNnKLgckV2lV42BoZj7WtH5DZj4HPBcRi7fompIkSZLmQasaB8s2r2TmgU2rw1t0TUmSJKlnDkiu1KqpTG+KiM/PWhgRXwBubtE1JUmSJM2DViUHXwN+FxGfAm4rZZsDCwOjWnRNSZIkqWcmB5Va0jjIzMnANhGxA7BRKb4kM69uxfUkSZIkzbuWPuegNAZsEEiSJGngpbMVVWnVmANJkiRJ85lWPyFZkiRJ6gyOOahkciBJkiQJMDmQJElSTaTJQSWTA0mSJEmAyYEkSZLqwuSgksmBJEmSJMDkQJIkSXXR5XMOqpgcSJIkSQJsHEiSJEkq7FYkSZKkenBAciWTA0mSJEmAyYEkSZLqwuSgksmBJEmSJMDkQJIkSTWRaXJQxeRAkiRJEmByIEmSpLpwzEElkwNJkiRJgMmBJEmS6sLkoJLJgSRJkiTA5ECSJEk1kSYHlUwOJEmSJAEmB5IkSaoLk4NKJgeSJEmSAJMDSZIk1UXXQFeg85kcSJIkSQJsHEiSJEkq7FYkSZKkWnAq02omB5IkSZIAkwNJkiTVhclBJZMDSZIkSYDJgSRJkurCqUwrmRxIkiRJAkwOJEmSVBPOVlTN5ECSJEkSYHIgSZKkunDMQSWTA0mSJEmAyYEkSZJqwjEH1UwOJEmSJAEmB5IkSaoLxxxUMjmQJEmSBJgcSJIkqSbS5KCSyYEkSZIkwMaBJEmSpMLGgSRJkuqhq41LhYhYPSKuiYj7IuLeiPhKKT8sIh6PiDvKskvTMYdExMSIeCAidm4qH1HKJkbEt5vK146Im0r52RExtKpeNg4kSZKk9psOfCMzNwS2Bg6IiA3LtqMzc9OyXApQtu0BbASMAH4eEYMiYhBwPDAS2BDYs+k8R5ZzrQs8D+xXVSkbB5IkSaqF7GrfUlmXzCcy87by+mXgfmDVXg7ZHTgrM1/LzIeBicCWZZmYmQ9l5lTgLGD3iAhgB+Dccvw4YFRVvWwcSJIkSf0sIsZExISmZUwv+64FvBO4qRQdGBF3RcQpEbFsKVsVeKzpsEmlrKfy5YEXMnP6LOW9snEgSZKkemjjmIPMHJuZWzQtY2dXpYhYAjgP+GpmvgScALwF2BR4AvhJP38KvfI5B5IkSdIAiIghNBoGv8rM8wEy86mm7ScBF5fVx4HVmw5frZTRQ/mzwDIRMbikB83798jkQJIkSbXQSWMOypiAk4H7M/OopvKVm3b7CHBPeX0hsEdELBwRawPrATcDtwDrlZmJhtIYtHxhZiZwDfCxcvxo4IKqepkcSJIkSe33HmAf4O6IuKOUfYfGbEObAgk8AnwBIDPvjYhzgPtozHR0QGbOAIiIA4HLgUHAKZl5bznfwcBZEXEEcDuNxkivotGo6DzTnnmoMysmSfNo0VW2HegqSFLLTJ/6eAx0HXoyecf3te3+coWr/tixn0Nv7FYkSZIkCbBbkSRJkmqiL2MB6s7kQJIkSRJgciBJkqS6yPlyGEBbmRxIkiRJAkwOJEmSVBOOOahmciBJkiQJsHEgSZIkqbBbkSRJkmohuxyQXMXkQJIkSRJgciBJkqSacEByNZMDSZIkSYDJgSRJkmoifQhaJZMDSZIkSYDJgSRJkmrCMQfVTA4kSZIkASYHkiRJqgmfc1DN5ECSJEkSYHIgSZKkmsgc6Bp0PpMDSZIkSYDJgSRJkmrCMQfVTA4kSZIkASYHkiRJqgmTg2omB5IkSZIAGweSJEmSCrsVSZIkqRacyrSayYEkSZIkwORAkiRJNeGA5GomB5IkSZIAkwNJkiTVRKbJQRWTA0mSJEmAyYEkSZJqIrsGugadz+RAkiRJEmByIEmSpJrocsxBJZMDSZIkSYDJgSRJkmrC2YqqVSYHEfHxiFiyvP5uRJwfEZu1vmqSJEmS2qkv3Yr+OzNfjoj3Ah8ATgZOaG21JEmSpP6VXdG2ZX7Vl8bBjPLvrsDYzLwEGNq6KkmSJEkaCH0Zc/B4RJwIfBA4MiIWxoHMkiRJms9kDnQNOl9fbvI/AVwO7JyZLwDLAd9saa0kSZIktV1fkoOVgUsy87WI2B54BzC+pbWSJEmS1HZ9SQ7OA2ZExLrAWGB14NctrZUkSZLUzxyQXK0vjYOuzJwOfBQ4LjO/SSNNkCRJkrQA6Uu3omkRsSewL/ChUjakdVWSJEmS+l+XD0Gr1Jfk4DPAu4EfZObDEbE2cHprqyVJkiSp3SqTg8y8Dzioaf1h4MhWVkqSJEnqb2lyUKmycRAR6wH/D9gQWKS7PDPXaWG9JEmSJLVZX8YcnAocChwNvJ9GNyMfgiZJkqT5ig9Bq9aXm/xFM/MqIDLz0cw8DNi1tdWSJEmS1G59SQ5ei4iFgL9FxIHA48ASra2WJEmS1L+crahaX5KDrwCL0RiUvDmwDzC6lZWSJEmS1H59ma3olvLyFRrjDSRJkqT5jrMVVeuxcRARFwE9DtvIzA+3pEaSJEmSBkRvycH/ta0WkiRJUos5W1G1HhsHmflHgFJr/+oAAB+4SURBVIhYHPhXZnaV9UHAwu2pniRJkqR26cuA5KtoDEjutijwh9ZUR5IkSWqNroy2LfOrvjQOFsnMV7pXyuvFetlfkiRJ0nyoL885mBIRm2XmbQARsTnwr9ZWC1ZeZ0SrLyFJA2L4YksPdBUkqZY6abaiiFgdGA+sSGMSoLGZeUxELAecDawFPAJ8IjOfj4gAjgF2AV4FPt10fz4a+G459RGZOa6Ubw6cRqPnz6XAVzJ7H3nRl+Tgq8BvIuL6iLihVPbAPr5vSZIkSW82HfhGZm4IbA0cEBEbAt8GrsrM9Wh07/922X8ksF5ZxgAnAJTGxKHAVsCWwKERsWw55gTg803HVf763qfnHETEBsBbS9EDmTmt8u1KkiRJmq3MfAJ4orx+OSLuB1YFdge2L7uNA64FDi7l48sv/zdGxDIRsXLZ98rMfA4gIq4ERkTEtcBSmXljKR8PjAIu661efelWRGkM3NPH9ypJkiR1nHYOFI6IMTR+4e82NjPH9rDvWsA7gZuAFUvDAeBJGt2OoNFweKzpsEmlrLfySbMp71WfGgeSJEmS+q40BGbbGGgWEUsA5wFfzcyXGkMLZp4jI6KtT2foy5gDSZIkab6XbVz6IiKG0GgY/Cozzy/FT5XuQpR/J5fyx4HVmw5frZT1Vr7abMp7Vdk4iIa9I+J/yvoaEbFl1XGSJEmSZq/MPnQycH9mHtW06UJgdHk9GrigqXzfcm++NfBi6X50ObBTRCxbBiLvBFxetr0UEVuXa+3bdK4e9aVb0c+BLmAH4HDgZRotnHf14VhJkiSpI3TYw8neA+wD3B0Rd5Sy7wA/As6JiP2AR4FPlG2X0pjGdCKNqUw/A5CZz0XE94Fbyn6Hdw9OBvbn9alML6NiMDL0rXGwVWZuFhG3lwo8HxFD+3CcJEmSpNnIzBuAnlorO85m/wQO6OFcpwCnzKZ8ArDxnNSrL42DaRExiNJ9KiKG00gSJEmSpPlGJz0ErVP1ZUDyscBvgRUi4gfADcAPW1orSZIkSW3Xl4eg/SoibqURbwQwKjPvb3nNJEmSpH5k15dqlY2DiFiDxqCHi5rLMvMfrayYJEmSpPbqy5iDS2iMNwhgEWBt4AFgoxbWS5IkSepX2eP4X3XrS7eitzevR8RmNKZFkiRJkrQA6Uty8AaZeVtEbNWKykiSJEmt0tXXRxfXWF/GHHy9aXUhYDPgny2rkSRJkqQB0ZfkYMmm19NpjEE4rzXVkSRJklqjyzEHlXptHJSHny2Zmf/ZpvpIkiRJGiA9PgQtIgZn5gzgPW2sjyRJkqQB0ltycDON8QV3RMSFwG+AKd0bM/P8FtdNkiRJ6jdOZVqtL2MOFgGeBXbg9ecdJGDjQJIkSVqA9NY4WKHMVHQPrzcKujkRlCRJkuYrXQNdgflAb42DQcASMNv8xcaBJEmStIDprXHwRGYe3raaSJIkSS3kmINqPc5WxOwTA0mSJEkLqN6Sgx3bVgtJkiSpxRxzUK3H5CAzn2tnRSRJkiQNrL5MZSpJkiTN90wOqvU25kCSJElSjZgcSJIkqRacraiayYEkSZIkwORAkiRJNdFlcFDJ5ECSJEkSYHIgSZKkmuhyzEElkwNJkiRJgI0DSZIkSYXdiiRJklQLOdAVmA+YHEiSJEkCTA4kSZJUE10DXYH5gMmBJEmSJMDkQJIkSTXRFU5lWsXkQJIkSRJgciBJkqSacLaiaiYHkiRJkgCTA0mSJNWEsxVVMzmQJEmSBJgcSJIkqSa6nKyoksmBJEmSJMDkQJIkSTXRhdFBFZMDSZIkSYDJgSRJkmrC5xxUMzmQJEmSBNg4kCRJklTYrUiSJEm14FSm1UwOJEmSJAEmB5IkSaqJroGuwHzA5ECSJEkSYHIgSZKkmnAq02omB5IkSZIAkwNJkiTVhLMVVTM5kCRJkgSYHEiSJKkmnK2omsmBJEmSJMDkQJIkSTVhclDN5ECSJEkSYHIgSZKkmkhnK6pkciBJkiQJsHEgSZKkmuhq49IXEXFKREyOiHuayg6LiMcj4o6y7NK07ZCImBgRD0TEzk3lI0rZxIj4dlP52hFxUyk/OyKGVtXJxoEkSZI0ME4DRsym/OjM3LQslwJExIbAHsBG5ZifR8SgiBgEHA+MBDYE9iz7AhxZzrUu8DywX1WFbBxIkiRJAyAzrwOe6+PuuwNnZeZrmfkwMBHYsiwTM/OhzJwKnAXsHhEB7ACcW44fB4yquoiNA0mSJNVCO7sVRcSYiJjQtIyZg6oeGBF3lW5Hy5ayVYHHmvaZVMp6Kl8eeCEzp89S3isbB5IkSVI/y8yxmblF0zK2j4eeALwF2BR4AvhJyyo5G05lKkmSpFrIga5AH2TmU92vI+Ik4OKy+jiwetOuq5Uyeih/FlgmIgaX9KB5/x6ZHEiSJEkdIiJWblr9CNA9k9GFwB4RsXBErA2sB9wM3AKsV2YmGkpj0PKFmZnANcDHyvGjgQuqrm9yIEmSpFro6rCHoEXEmcD2wLCImAQcCmwfEZvSCDoeAb4AkJn3RsQ5wH3AdOCAzJxRznMgcDkwCDglM+8tlzgYOCsijgBuB06urFOjUdF5hi21fmdWTJLm0ZCF/F1G0oLriRfu67Bb8Ncds8bebbu//Mo/zujYz6E3/j+UJEmSaqGvDyerM8ccSJIkSQJMDiRJklQTJgfVTA4kSZIkASYHkiRJqglnu6lmciBJkiQJMDmQJElSTXTacw46kcmBJEmSJMDkQJIkSTXhbEXVTA4kSZIkATYOJEmSJBV2K5IkSVItOJVpNZMDSZIkSYDJgSRJkmqiy+ygksmBJEmSJMDkQJIkSTXhVKbVTA4kSZIkASYHkiRJqglHHFQzOZAkSZIEmBxIkiSpJhxzUM3kQJIkSRJgciBJkqSa6IqBrkHnMzmQJEmSBJgcSJIkqSZ8QnI1kwNJkiRJgMmBJEmSasLcoJrJgSRJkiTAxoEkSZKkwm5FkiRJqgUfglbN5ECSJEkSYHIgSZKkmnAq02omB5IkSZIAkwNJkiTVhLlBNZMDSZIkSYDJgSRJkmrC2YqqmRxIkiRJAkwOJEmSVBPOVlTN5ECSJEkSYHIgSZKkmjA3qGZyIEmSJAkwOZAkSVJNOFtRNZMDSZIkSYDJgSRJkmoiHXVQyeRAkiRJEmDjQJIkSVJhtyJJkiTVggOSq5kcSJIkSQJMDiRJklQTXQ5IrmRyIEmSJAkwOZAkSVJNmBtUMzmQJEmSBJgcSJIkqSYcc1DN5ECSJEkSYHIgSZKkmvA5B9VMDiRJkiQBNg60AFtl1ZX43cXj+dPNl3LDTZcw5kv7ArDMsktz7u9O5ebbr+Dc353K0sssBcCBB+3HNTdcwDU3XMD1N17MU8/fzzLLLg3AMcf/kPv//heuv/HiAXs/ktRtlVVX4tyLTuWPN17EtX+5kM99ce+Z2z47Zi+uv/lirv3LhXz3e98AYLvt383l1/6Gq//0Oy6/9je8Z7ut3nTO0878Gdf8+YK2vQdpIGQb/zO/iszOrPywpdbvzIppvrHiisNZcaXh3HXnfSyxxOJcdd357LPn/uy510d5/vkXOfbosRz0tTEss8xSHH7o/73h2J1HvJ8vHvBpPvKh0QC8e5stmDLlVY4/8cdsu/VuA/F2tAAZspA9OjVvVlhxGCuuNJy777yfxZdYjMuvPZfP7vVlhq2wPF/5xhfY5xNfZOrUaSw/bDmefeY5Nn7H23h68jM89eTTvPVt63LmeSex2Ybvn3m+XT70AXb98E5suNFbef82uw/gO9OC4IkX7ouBrkNPPrfWx9p2f/nLR87t2M+hNyYHWmA99dTT3HXnfQC88soUHnzg76y8yoqM3HVHzv71bwE4+9e/ZZfdPvCmYz/68d04/9xLZq7/5c8TeP75F9tTcUmqMPmpZ7j7zvsBmPLKq/ztwYdYaeUVGP3ZPfjZ0b9k6tRpADz7zHMA3HPX/Tz15NMAPHD/RBZZdBGGDh0CwGKLL8YX9h/NMf934gC8E6m9utq4zK9a1jiIiA9FhI0PdYTV11iVt79jQ26dcCfDhw/jqaca/yf51FNPM3z4sDfsu+iii7DDB7blogsvH4iqStIcWW2NVXj729/GbbfexTrrrsVW22zOJX84i/MvGccm79z4Tfvv+uGduPvO+2Y2IA7+ry/zi+NP49V//avdVZdqLyJOiYjJEXFPU9lyEXFlRPyt/LtsKY+IODYiJkbEXRGxWdMxo8v+f4uI0U3lm0fE3eWYYyOiMs1o5c37J4G/RcSPI2KDvhwQEWMiYkJETPj3VH+lVf9YfPHFOO304/ivb/+QV16e8qbts3at23nkDtx84228YFIgqcMttvhinDz+GP7nO/+PV16ewuBBg1hm2aXZ9QN7cPh//x9jTzvqDfuvv8G6fPd7X+dbXz0MgI3evgFrrr06l1181QDUXmq/DhxzcBowYpaybwNXZeZ6wFVlHWAksF5ZxgAnQKMxARwKbAVsCRza3aAo+3y+6bhZr/UmLWscZObewDuBvwOnRcRfys3/kr0cMzYzt8jMLRYZunSrqqYaGTx4MKeecRznnnMRl1x0BQBPP/0MK644HGiMS3jmmWffcMxH/mNXzj/XgceSOtvgwYM5efxPOf83F3PpRX8A4Il/PsmlF10JwB233U1XVxfLL9+4R1h5lRU55YxjOeiLh/DoI48BsPm7NmGTTTfm5ruu5ILLzmCdddfivItPG5D3I9VRZl4HPDdL8e7AuPJ6HDCqqXx8NtwILBMRKwM7A1dm5nOZ+TxwJTCibFsqM2/Mxi+h45vO1aOWdvvJzJeAc4GzgJWBjwC3RcSXW3ldqdsxx/+QBx/4Oyccf+rMst9fejWf/NRHAPjkpz7CZZe8/ovZkkstwTbvfdcbyiSpEx31s+/ztwcf4sTjx80s+/0lV/OebbcEYJ23rMmQIUN49tnnWWrpJTn9nBP44feO4pabbp+5//hTzuadb9ueLd/xQXYfuTcPTXyE/9jt0+1+K9ICqblHTFnG9PHQFTPzifL6SWDF8npV4LGm/SaVst7KJ82mvFetHHPw4Yj4LXAtMATYMjNHApsA32jVdaVuW229OZ/ccxTbbrf1zClKP7DT+zjm6LFs//73cPPtV/C+7bfhmKPHzjxm190+yLVX/4lXX31j39uxpxzF7/9wNuuutzZ33X8de+3zsXa/HUmaacutN+Pje+zOe7bbiiuvP58rrz+fHT64HWeecT5rrrk61/z5An5xyk/4yv7fAeCzn/8Ua6+9Bl/71v4z919+2HID/C6k9mvngOTmHjFlef2Go4/KL/5tncGzZVOZRsQ44OQSl8y6bcfM7PWnWacylbSgcipTSQuyTp7KdPRa/9G2+8txj5zXp88hItYCLs7Mjcv6A8D2mflE6Rp0bWa+NSJOLK/PbN6ve8nML5TyE2n8OH8tcE1mblDK92zeryetHHMwenYNg7LNPhuSJElqq67Mti3z4EKge8ah0cAFTeX7llmLtgZeLN2PLgd2iohly0DknYDLy7aXImLrMkvRvk3n6lG//3wVES/zxvgjynrQSEeW6u9rSpIkSfObiDiTxi//wyJiEo1Zh34EnBMR+wGPAp8ou18K7AJMBF4FPgOQmc9FxPeBW8p+h2dm9yDn/WnMiLQocFlZeq+TT0iWpPayW5GkBVkndyvae82Ptu3+8oxHz+/Yz6E3Lf9/qIhYAVikez0z/9Hqa0qSJEmacy1rHETEh4GfAKsAk4E1gfuBjVp1TUmSJKknXe2d+Ge+1MrnHHwf2Bp4MDPXBnYEbmzh9SRJkiTNg1Y2DqZl5rPAQhGxUGZeA2zRwutJkiRJPco2/md+1coxBy9ExBLAdcCvImIyMKWF15MkSZI0D1rZONgd+DfwNWAvYGng8BZeT5IkSepR10BXYD7QssZBZk4BiIilgItadR1JkiRJ/aOVsxV9AfgejfSgi9cfhrZOq64pSZIk9cTZiqq1slvRfwIbZ+YzLbyGJEmSpH7SysbB32k82lmSJEkacPPzLELt0srGwSHAnyPiJuC17sLMPKiF15QkSZI0l1rZODgRuBq4GweHS5IkSR2vlY2DIZn59RaeX5IkSeozf62u1sonJF8WEWMiYuWIWK57aeH1JEmSJM2DViYHe5Z/D2kqcypTSZIkDYhMByRXaeVD0NZu1bklSZIk9b9WPgRtCPAlYLtSdC1wYmZOa9U1JUmSpJ74ELRqrexWdAIwBPh5Wd+nlH2uhdeUJEmSNJda2Th4V2Zu0rR+dUTc2cLrSZIkST1ytqJqrZytaEZEvKV7JSLWAWa08HqSJEmS5kErk4NvAtdExENAAGsCn2nh9SRJkqQepWMOKrVytqKrImI94K2l6IHMfK1V15MkSZI0b1qZHABsDqxVrrNpRJCZ41t8TUmSJOlNnK2oWiunMj0deAtwB6+PNUjAxoEkSZLUgVqZHGwBbJg+ik6SJEkdwNvSaq2cregeYKUWnl+SJElSP2plcjAMuC8ibga6ByJnZu7ewmtKkiRJs+VzDqq1snFwWNPrALYF9mjh9SRJkiTNg1ZOZfrHiHgn8Cng48DDwC9adT1JkiSpNz7noFq/Nw4iYn1gz7I8A5wNRGa+v7+vJUmSJKn/tCI5+CtwPbBbZk4EiIivteA6kiRJkvpRKxoHH6UxtuCaiPg9cBaNMQeSJEnSgPEhaNX6fSrTzPxdZu4BbABcA3wVWCEiToiInfr7epIkSZL6R8uec5CZUzLz15n5IWA14Hbg4FZdT5IkSepNZrZtmV+18iFoM2Xm85k5NjN3bMf1JEmSJM25Vj7nQJIkSeoYjjmo1pbkQJIkSVLnMzmQJElSLfgQtGomB5IkSZIAkwNJkiTVRNd8PItQu5gcSJIkSQJMDiRJklQT5gbVTA4kSZIkASYHkiRJqgmfc1DN5ECSJEkSYHIgSZKkmjA5qGZyIEmSJAmwcSBJkiSpsFuRJEmSaiF9CFolkwNJkiRJgMmBJEmSasIBydVMDiRJkiQBJgeSJEmqiTQ5qGRyIEmSJAkwOZAkSVJNOFtRNZMDSZIkSYDJgSRJkmrC2YqqmRxIkiRJAkwOJEmSVBOOOahmciBJkiQNgIh4JCLujog7ImJCKVsuIq6MiL+Vf5ct5RERx0bExIi4KyI2azrP6LL/3yJi9LzUycaBJEmSaqGLbNsyB96fmZv+//buNMauso7j+PcHLkVB3LAhoCzKIotBqEJcKqhB0BhAiYgLSyBVQzAajfoOxbjFLQZURERAQRCxikFpAaMtBKEspYUCbgUFFdQqARVQ5u+L81y9qTO97djOtHO+n2Yy5z73LM/tizP3f37Pc05VzWmvPwRcVVW7AFe11wCHAru0n3nAl6ErJoBTgP2BFwOnDAqKybA4kCRJkjYehwHntuVzgcOH2s+rzs+ApybZFngNcEVVraqqvwBXAIdM9uAWB5IkSeqFmsJ/SeYluWHoZ964XYKFSW4cen92Vf2+Lf8BmN2WtwN+O7TtPa1tovZJcUKyJEmStJ5V1ZnAmSNWe1lV3ZvkWcAVSe5YbR+VZEpnUZscSJIkSdOgqu5tv+8H5tPNGbivDRei/b6/rX4v8OyhzbdvbRO1T4rFgSRJknphrGrKfkZJ8uQkWw2WgYOBW4FLgcEdh44Fvt+WLwWOaXctOgB4oA0/WgAcnORpbSLywa1tUhxWJEmSJE292cD8JNB9J7+gqi5PsgT4dpITgLuBN7X1fwi8Fvgl8HfgeICqWpXko8CStt6pVbVqsp3KxvowiGc+ZdeNs2OS9H96/GZel5E0c/3+rysy3X2YyJ6z95+y75e33XfdRvv/sCYOK5IkSZIEOKxIkiRJPbE2cwH6zuRAkiRJEmByIEmSpJ4oTA5GMTmQJEmSBJgcSJIkqSecczCayYEkSZIkwORAkiRJPeGcg9FMDiRJkiQBJgeSJEnqCeccjGZyIEmSJAkwOZAkSVJPOOdgNJMDSZIkSYDFgSRJkqTGYUWSJEnqhaqx6e7CRs/kQJIkSRJgciBJkqSeGHNC8kgmB5IkSZIAkwNJkiT1RPkQtJFMDiRJkiQBJgeSJEnqCeccjGZyIEmSJAkwOZAkSVJPOOdgNJMDSZIkSYDJgSRJknpizORgJJMDSZIkSYDJgSRJknqivFvRSCYHkiRJkgCTA0mSJPWEdysazeRAkiRJEmBxIEmSJKlxWJEkSZJ6YcwJySOZHEiSJEkCTA4kSZLUE05IHs3kQJIkSRJgciBJkqSeGDM5GMnkQJIkSRJgciBJkqSecM7BaCYHkiRJkgCTA0mSJPWEzzkYzeRAkiRJEmByIEmSpJ5wzsFoJgeSJEmSAJMDSZIk9YTPORjN5ECSJEkSYHIgSZKknijvVjSSyYEkSZIkwOJAkiRJUuOwIkmSJPWCE5JHMzmQJEmSBJgcSJIkqSd8CNpoJgeSJEmSAJMDSZIk9YS3Mh3N5ECSJEkSYHIgSZKknnDOwWgmB5IkSZIAkwNJkiT1hMnBaCYHkiRJkgCTA0mSJPWEucFoJgeSJEmSAIhjryRIMq+qzpzufkjShuA5TtLaMjmQOvOmuwOStAF5jpO0ViwOJEmSJAEWB5IkSZIaiwOp41hcSTOZ5zhJa8UJyZIkSZIAkwNJkiRJjcWBJEmSJMDiQDNYktlJLkjy6yQ3Jrk2yRHrYb8/STJnffRRksaT5PAklWT3aezDQ9N1bEnTx+JAM1KSAN8DFlXVzlW1H/BmYPvp7ZkkrZWjgavbb0maMhYHmqleCTxaVWcMGqrq7qo6LcmsJF9PsjzJzUkOAlhD+xZJLkxye5L5wBbT85Ek9UGSLYGXASfQXdQgyYEttfxOkjuSnN8ugpDkVe2ctTzJ2Ume2NrvSvKJJEuT3JBk3yQLkvwqyTsHx0pyVZKb2vaHjdOf85IcPvT6/PHWkzQzWBxoptoTuGmC904Cqqr2prsqd26SWWtofxfw96p6PnAKsN8G772kPjsMuLyqfg78OcngnPNC4D3AHsDOwEvbOeoc4Kh27noc3Tlr4DdVtQ+wuK13JHAA8JH2/sPAEVW1L3AQ8NlB0THka8BxAEm2Bl4CXLa+PqykjYvFgXohyReT3JJkCd0VuW8CVNUdwN3ArmtonzvUvgxYNuUfQFKfHA1c2JYv5L9Di66vqnuqagxYCuwI7AasbIUEwLl056yBS9vv5cB1VfVgVf0ReCTJU4EAH0+yDLgS2A6YPdyZqvopsEuSbVpfLqmqf623Tytpo/K46e6AtIHcBrxx8KKqTkryTOAG4J5p65UkrUGSp9MNi9w7SQGbA0V3pf6RoVUfY+3+hg+2GVtt+7G2/VuBbYD9quqfSe4CZo2zn/OAt9ENczp+bT+PpE2PyYFmqh8Ds5IMx+tPar8X0/1BJMmuwHOAO9fQvgh4S2vfC3jBFPRfUj8dCXyjqnaoqh2r6tnASuDlE6x/J7Bjkue1128HfroOx9sauL8VBgcBO0yw3jl0Q5qoqhXrsH9JmxiLA81I1T36+3DgFUlWJrmeLm7/IPAlYLMky4GLgOOq6pE1tH8Z2DLJ7cCpwI1T/4kk9cTRwPzV2i5hgrsWVdXDdFfyL27nrjHgjPHWncD5wJy27THAHRMc5z7gduDr67BvSZugdN+hJEmSxpfkSXTzFvatqgemuz+SNhyTA0mSNKEkr6ZLDU6zMJBmPpMDSZIkSYDJgSRJkqTG4kCSJEkSYHEgSZIkqbE4kDTjJXksydIktya5uN15ZbL7OifJkW35rCR7rGHdA5O8ZBLHuKs9tG9t1j0uyenregxJksZjcSCpD/5RVftU1V7Ao8A7h99MMqmnxVfViSMeCHUgsM7FgSRJ08XiQFLfLAae167qL05yKbAiyeZJPp1kSZJlSd4BkM7pSe5MciXwrMGOkvwkyZy2fEiSm5LckuSqJDvSFSHvbanFy5Nsk+SSdowlSV7atn1GkoVJbktyFpDxOr76McZ5//VJrktyc5Irk8xu7a9ofVja3tsqybZJFg0lKhM9gVeS1COTulomSZuilhAcClzemvYF9qqqlUnmAQ9U1YuSPBG4JslC4IXAbsAewGxgBXD2avvdBvgqMLft6+lVtSrJGcBDVfWZtt4FwOer6uokzwEWAM8HTgGurqpTk7wOOGGcvv/PMcb5iFcDB1RVJTkR+ADwPuD9wElVdU2SLYGHgXnAgqr6WJLNgUkPtZIkzRwWB5L6YIskS9vyYuBrdMN9rq+qla39YOAFg/kEwNbALsBc4FtV9RjwuyQ/Hmf/BwCLBvuqqlUT9OPVwB7Jf4KBp7Qv63OBN7RtL0vyl0keY3vgoiTbAk8ABp/tGuBzSc4HvltV9yRZApyd5PHA96pq6Tj7kyT1jMOKJPXBYM7BPlV1clU92tr/NrROgJOH1tupqhau535sRndlf3CM7arqofW4/9OA06tqb+AdwCyAqvokcCKwBV0isntVLaIrSu4FzklyzHrshyRpE2VxIEmdBcC72pV0kuya5MnAIuCoNidhW+Cgcbb9GTA3yU5t28GQnweBrYbWWwicPHiRZJ+2uAh4S2s7FHjaOhxj2NZ0X/YBjh06znOranlVfQpYAuyeZAfgvqr6KnAW3RArSVLPWRxIUucsuvkENyW5FfgK3dDL+cAv2nvnAdeuvmFV/ZFuDP93k9wCXNTe+gFwxGBCMvBuYE6b8LyC/9416SN0X/xvoxte9Jt1OMawDwMXJ7kR+NNQ+3vapONlwD+BH9HdSemWJDcDRwFfGP1fJEma6VJV090HSZIkSRsBkwNJkiRJgMWBJEmSpMbiQJIkSRJgcSBJkiSpsTiQJEmSBFgcSJIkSWosDiRJkiQB8G/zrt84TMmflQAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 1008x720 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"aL7ZfN2lnFZe","colab_type":"code","outputId":"738eaea3-66b3-47ea-f9a0-7dad1b95f8a0","executionInfo":{"status":"ok","timestamp":1589162581819,"user_tz":-330,"elapsed":6676,"user":{"displayName":"PRITAM PALIT","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Giix9Z-rAuOW8-Wch3FyW7hklDgx0X8ExfVyr5q5A=s64","userId":"02421712431687232718"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["rcParams['figure.figsize'] = 14, 10\n","#Scatter plot for Train\n","error_df_train = error_df_train.reset_index(drop=True)\n","threshold_fixed_train = 0.000014\n","groups = error_df_train.groupby('true_class')\n","fig, ax = plt.subplots()\n","for name, group in groups:\n","    ax.plot(group.index, group.reconstruction_error, marker='o', ms=3.5, linestyle='', \n","            label= \"Anomaly\" if name == 1 else \"Good\")\n","ax.hlines(threshold_fixed_train, ax.get_xlim()[0], ax.get_xlim()[1], colors=\"r\", zorder=100, label='Threshold = 0.000014')\n","ax.legend()\n","plt.title(\"Reconstruction error for different classes for Train\")\n","plt.ylabel(\"Reconstruction error\")\n","plt.xlabel(\"Data point index\")\n","#plt.show();\n","\n","#Scatter plot for Validation\n","error_df_valid = error_df_valid.reset_index(drop=True)\n","threshold_fixed_valid = 0.000014\n","groups = error_df_valid.groupby('true_class')\n","fig, ax = plt.subplots()\n","\n","for name, group in groups:\n","    ax.plot(group.index, group.reconstruction_error, marker='o', ms=3.5, linestyle='', \n","            label= \"Anomaly\" if name == 1 else \"Good\")\n","ax.hlines(threshold_fixed_valid, ax.get_xlim()[0], ax.get_xlim()[1], colors=\"r\", zorder=100, label='Threshold = 0.000014')\n","ax.legend()\n","plt.title(\"Reconstruction error for different classes for Valid\")\n","plt.ylabel(\"Reconstruction error\")\n","plt.xlabel(\"Data point index\")\n","#plt.show();\n","\n","\n","error_df_test = error_df_test.reset_index(drop=True)\n","threshold_fixed_test = 0.000013\n","groups = error_df_test.groupby('true_class')\n","gmean= 3.100298e-05 \n","gstd = 1.339863e-04\n","fig, ax = plt.subplots()\n","\n","for name, group in groups:\n","    ax.plot(group.index, group.reconstruction_error, marker='o', ms=3.5, linestyle='', \n","            label= \"Bad\" if name == 1 else \"Good\")\n","ax.hlines(threshold_fixed_test, ax.get_xlim()[0], ax.get_xlim()[1], colors=\"k\", zorder=100, label='Threshold = 0.000013')\n","#ax.hlines(gmean, ax.get_xlim()[0], ax.get_xlim()[1], color=\"blue\", label=\"Run average: \" + str(gmean))\n","ax.hlines(gmean+(1.0*gstd), ax.get_xlim()[0], ax.get_xlim()[1], color=\"red\", label='1 SD (' + str(gstd) + \")\")\n","ax.hlines(gmean+(3.0*gstd), ax.get_xlim()[0], ax.get_xlim()[1], color=\"red\", label='3 SD', linestyle=':')    \n","ax.legend()\n","plt.title(\"Reconstruction error for different classes for Test\")\n","plt.ylabel(\"Reconstruction error\")\n","plt.xlabel(\"Data point index\")\n","plt.show();\n","\n","error_df_all = error_df_all.reset_index(drop=True)\n","threshold_fixed_all = 0.000014\n","groups = error_df_all.groupby('true_class')\n","#gmean= 3.100298e-05 \n","#gstd = 1.339863e-04\n","fig, ax = plt.subplots()\n","\n","for name, group in groups:\n","    ax.plot(group.index, group.reconstruction_error, marker='o', ms=3.5, linestyle='', \n","            label= \"Bad\" if name == 1 else \"Good\")\n","ax.hlines(threshold_fixed_all, ax.get_xlim()[0], ax.get_xlim()[1], colors=\"k\", zorder=100, label='Threshold = 0.000014')\n","#ax.hlines(gmean, ax.get_xlim()[0], ax.get_xlim()[1], color=\"blue\", label=\"Run average: \" + str(gmean))\n","ax.hlines(gmean+(1.0*gstd), ax.get_xlim()[0], ax.get_xlim()[1], color=\"red\", label='1 SD (' + str(gstd) + \")\")\n","ax.hlines(gmean+(3.0*gstd), ax.get_xlim()[0], ax.get_xlim()[1], color=\"red\", label='3 SD', linestyle=':')    \n","ax.legend()\n","#plt.scatter(df['fromrun'].unique(), y, marker='+', label='Data points')\n","plt.ylim([0.0, 0.0006])\n","plt.title(\"Reconstruction error for different classes for Test\")\n","plt.ylabel(\"Reconstruction error\")\n","plt.xlabel(\"Data point index\")\n","plt.show();\n"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1008x720 with 1 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\n","text/plain":["<Figure size 1008x720 with 1 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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 1008x720 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"GBVpK81P8UmV","colab_type":"code","outputId":"31bce767-f7cc-4f43-deb1-1b0b796f2eb2","executionInfo":{"status":"ok","timestamp":1589163398648,"user_tz":-330,"elapsed":1405,"user":{"displayName":"PRITAM PALIT","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Giix9Z-rAuOW8-Wch3FyW7hklDgx0X8ExfVyr5q5A=s64","userId":"02421712431687232718"}},"colab":{"base_uri":"https://localhost:8080/","height":104}},"source":["df['mse2']=mse_all\n","df['mse2'].loc[df['labels']==True]=mse_good_all\n","df['mse2'].loc[df['labels']==False]=mse_bad_all\n","def globalMSETrend(df):\n","    \n","    y=[]\n","    \n","    rmax=0\n","    rmin=10\n","    \n","    print(rmax, rmin)\n","    for run in df['fromrun'].unique():\n","        #if (type==1): val=(df['mse'][run]).mean()\n","        val=(df['mse2'][run]).mean()\n","        if val > rmax: rmax=val\n","        if val < rmin: rmin =val\n","        y.append(val)\n","    \n","    array=np.array(y)\n","    gmean=array.mean()\n","    size=float(array.size)\n","    gstd=array.std()\n","    print(size)\n","    \n","    fig=plt.figure()\n","    fig.set_size_inches(16, 9)\n","    \n","    plt.hlines(gmean,df['fromrun'].unique().min(), df['fromrun'].unique().max(), color=\"blue\", label=\"Run average: \" + str(gmean))\n","    plt.hlines(gmean+(1.0*gstd), df['fromrun'].unique().min(), df['fromrun'].unique().max(), color=\"red\", label='1 SD (' + str(gstd) + \")\")\n","    plt.hlines(gmean+(3.0*gstd), df['fromrun'].unique().min(), df['fromrun'].unique().max(), color=\"red\", label='3 SD', linestyle=':')\n","    \n","    plt.ylim(rmin*0.9,rmax*1.1)\n","    plt.scatter(df['fromrun'].unique(), y, marker='+', label='Data points')\n","    plt.xlabel(\"Run\")\n","    plt.ylabel(\"average MSE\")\n","    plt.legend()\n","    plt.show()"],"execution_count":0,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py:671: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\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","  self._setitem_with_indexer(indexer, value)\n"],"name":"stderr"}]},{"cell_type":"code","metadata":{"id":"RkXlqB3HGB7Q","colab_type":"code","colab":{}},"source":["def globalMSETrendAll(df):\n","    dff = df[df.labels==False]\n","    yf=[]\n","    #print (dff.head())\n","    rmax=0\n","    rmin=10\n","    \n","    #print(rmax, rmin)\n","    for run in dff['fromrun'].unique():\n","        #if (type==1): val=(dff['mse'][run]).mean()\n","        val=(dff['mse2'][run]).mean()\n","        if val > rmax: rmax=val\n","        if val < rmin: rmin =val\n","        yf.append(val)\n","    \n","    arrayf=np.array(yf)\n","    gmeanf=arrayf.mean()\n","    sizef=float(arrayf.size)\n","    gstdf=arrayf.std()\n","    print(sizef)\n","##################\n","    dft = df[df.labels==True]\n","    y=[]\n","    \n","    rmax=0\n","    rmin=10\n","    \n","    print(rmax, rmin)\n","    for run in dft['fromrun'].unique():\n","        #if (type==1): val=(dft['mse'][run]).mean()\n","        val=(dft['mse2'][run]).mean()\n","        if val > rmax: rmax=val\n","        if val < rmin: rmin =val\n","        y.append(val)\n","    \n","    array=np.array(y)\n","    gmean=array.mean()\n","    size=float(array.size)\n","    gstd=array.std()\n","    print(size)\n","    \n","#######################\n","\n","    fig=plt.figure()\n","    fig.set_size_inches(16, 9)\n","    \n","    plt.hlines(gmean,dft['fromrun'].unique().min(), dft['fromrun'].unique().max(), color=\"blue\", label=\"Good Run average: \" + str(gmean))\n","    plt.hlines(threshold_fixed_all, dft['fromrun'].unique().min(), dft['fromrun'].unique().max(), colors=\"k\", zorder=100, label='Threshold = 0.000014')\n","    plt.hlines(gmean+(1.0*gstd), dft['fromrun'].unique().min(), dft['fromrun'].unique().max(), color=\"red\", label='1 SD (' + str(gstd) + \")\")\n","    plt.hlines(gmean+(3.0*gstd), dft['fromrun'].unique().min(), dft['fromrun'].unique().max(), color=\"red\", label='3 SD', linestyle=':')\n","    \n","    plt.hlines(gmeanf,dff['fromrun'].unique().min(), dff['fromrun'].unique().max(), color=\"green\", label=\"Bad Run average: \" + str(gmeanf))\n","    #plt.hlines(gmeanf+(1.0*gstd), dff['fromrun'].unique().min(), dff['fromrun'].unique().max(), color=\"green\", label='1 SD (' + str(gstdf) + \")\")\n","    #plt.hlines(gmeanf+(3.0*gstd), dff['fromrun'].unique().min(), dff['fromrun'].unique().max(), color=\"green\", label='3 SD', linestyle=':')\n","    \n","    \n","    #plt.ylim(rmin*0.9,rmax*1.1)\n","    plt.ylim([0.0,0.0007])\n","    plt.scatter(dft['fromrun'].unique(), y, marker='+', label='Good Data points')\n","    plt.scatter(dff['fromrun'].unique(), yf, marker='+', label='Bad Data points',color=\"yellow\")\n","    plt.xlabel(\"Run\")\n","    plt.ylabel(\"average MSE\")\n","    plt.legend(bbox_to_anchor=(0, 1), loc='lower left', ncol=1)\n","    plt.show()\n","\t\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"2gZCWonoy4Kz","colab_type":"code","outputId":"fc160be8-c824-4b46-d0e6-6672ee11b9ba","executionInfo":{"status":"ok","timestamp":1589163470573,"user_tz":-330,"elapsed":2549,"user":{"displayName":"PRITAM PALIT","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Giix9Z-rAuOW8-Wch3FyW7hklDgx0X8ExfVyr5q5A=s64","userId":"02421712431687232718"}},"colab":{"base_uri":"https://localhost:8080/","height":716}},"source":["globalMSETrendAll(df)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["465.0\n","0 10\n","469.0\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1152x648 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"RdKgkpjq-aXg","colab_type":"code","outputId":"953d8491-ec64-4d5c-b78d-5e07299be614","executionInfo":{"status":"error","timestamp":1589070777919,"user_tz":-330,"elapsed":1580,"user":{"displayName":"PRITAM PALIT","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Giix9Z-rAuOW8-Wch3FyW7hklDgx0X8ExfVyr5q5A=s64","userId":"02421712431687232718"}},"colab":{"base_uri":"https://localhost:8080/","height":202}},"source":["print(X_train_true_norm.shape)\n","print(X_valid_true_norm.shape)\n","print(X_test_true_norm.shape)"],"execution_count":0,"outputs":[{"output_type":"error","ename":"NameError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-1-4b17a8c5d91e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train_true_norm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\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      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_valid_true_norm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test_true_norm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\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 'X_train_true_norm' is not defined"]}]},{"cell_type":"code","metadata":{"id":"WBUvZNHAr28Y","colab_type":"code","outputId":"40f507c6-86ab-4a72-b90f-db1296597540","executionInfo":{"status":"ok","timestamp":1588803581566,"user_tz":-330,"elapsed":1298,"user":{"displayName":"PRITAM PALIT","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Giix9Z-rAuOW8-Wch3FyW7hklDgx0X8ExfVyr5q5A=s64","userId":"02421712431687232718"}},"colab":{"base_uri":"https://localhost:8080/","height":382}},"source":["#LSTM\n","from numpy import array\n","from keras.models import Sequential\n","from keras.models import Sequential\n","from keras import optimizers\n","from keras.layers import LSTM\n","from keras.layers import Dense\n","from keras.layers import RepeatVector\n","from keras.layers import TimeDistributed\n","from keras.utils import plot_model\n","from keras.layers.advanced_activations import ReLU\n","\n","lr = 0.0001\n","# reshape input into [samples, timesteps, features]\n","timesteps = len(X_train_true_norm[0])\n","n_samples = X_train_true_norm.shape[0]\n","n_features = 1\n","X_train_lstm = X_train_true_norm.reshape((n_samples, timesteps, 1))\n","print(X_train_lstm.shape)\n","\n","timesteps_valid = len(X_valid_true_norm[0])\n","n_samples_valid = X_valid_true_norm.shape[0]\n","n_features = 1\n","X_valid_lstm = X_valid_true_norm.reshape((n_samples_valid, timesteps_valid, 1))\n","\n","lstm_autoencoder = Sequential()\n","# Encoder\n","lstm_autoencoder.add(LSTM(10, activation='tanh', input_shape=(timesteps, n_features), return_sequences=True))\n","lstm_autoencoder.add(LSTM(3, activation='tanh', return_sequences=False))\n","lstm_autoencoder.add(RepeatVector(timesteps))\n","# Decoder\n","lstm_autoencoder.add(LSTM(3, activation='tanh', return_sequences=True))\n","lstm_autoencoder.add(LSTM(10, activation='tanh', return_sequences=True))\n","lstm_autoencoder.add(TimeDistributed(Dense(n_features)))\n","\n","lstm_autoencoder.summary()\n","\n"],"execution_count":0,"outputs":[{"output_type":"stream","text":["(129382, 100, 1)\n","Model: \"sequential_1\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","lstm_1 (LSTM)                (None, 100, 10)           480       \n","_________________________________________________________________\n","lstm_2 (LSTM)                (None, 3)                 168       \n","_________________________________________________________________\n","repeat_vector_1 (RepeatVecto (None, 100, 3)            0         \n","_________________________________________________________________\n","lstm_3 (LSTM)                (None, 100, 3)            84        \n","_________________________________________________________________\n","lstm_4 (LSTM)                (None, 100, 10)           560       \n","_________________________________________________________________\n","time_distributed_1 (TimeDist (None, 100, 1)            11        \n","=================================================================\n","Total params: 1,303\n","Trainable params: 1,303\n","Non-trainable params: 0\n","_________________________________________________________________\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"zE_qfvj9llTE","colab_type":"code","outputId":"3c58dca0-8f0f-41a1-ace3-d3498113d881","executionInfo":{"status":"ok","timestamp":1588821290651,"user_tz":-330,"elapsed":17700169,"user":{"displayName":"PRITAM PALIT","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Giix9Z-rAuOW8-Wch3FyW7hklDgx0X8ExfVyr5q5A=s64","userId":"02421712431687232718"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["epochs=100\n","batch=500\n","\n","adam = optimizers.Adam(lr)\n","lstm_autoencoder.compile(loss='mse', optimizer=adam)\n","\n","cp = ModelCheckpoint(filepath=\"/content/drive/My Drive/Colab Notebooks/lstm_autoencoder_classifier.h5\",\n","                               save_best_only=True,\n","                               verbose=0)\n","\n","tb = TensorBoard(log_dir='/content/drive/My Drive/Colab Notebooks/lstm_logs',\n","                histogram_freq=0,\n","                write_graph=True,\n","                write_images=True)\n","\n","lstm_autoencoder_history = lstm_autoencoder.fit(X_train_lstm, X_train_lstm, \n","                                                epochs=epochs, \n","                                                batch_size=batch, \n","                                                validation_data=(X_valid_lstm, X_valid_lstm),\n","                                                verbose=2).history"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Train on 129382 samples, validate on 32421 samples\n","Epoch 1/100\n"," - 181s - loss: 2.1108e-04 - val_loss: 1.9895e-04\n","Epoch 2/100\n"," - 179s - loss: 1.1894e-04 - val_loss: 3.4616e-05\n","Epoch 3/100\n"," - 176s - loss: 3.1598e-05 - val_loss: 3.0539e-05\n","Epoch 4/100\n"," - 176s - loss: 3.0371e-05 - val_loss: 3.0077e-05\n","Epoch 5/100\n"," - 176s - loss: 3.0051e-05 - val_loss: 2.9879e-05\n","Epoch 6/100\n"," - 176s - loss: 2.9889e-05 - val_loss: 2.9773e-05\n","Epoch 7/100\n"," - 176s - loss: 2.9783e-05 - val_loss: 2.9677e-05\n","Epoch 8/100\n"," - 176s - loss: 2.9703e-05 - val_loss: 2.9597e-05\n","Epoch 9/100\n"," - 177s - loss: 2.9646e-05 - val_loss: 2.9574e-05\n","Epoch 10/100\n"," - 176s - loss: 2.9580e-05 - val_loss: 2.9474e-05\n","Epoch 11/100\n"," - 176s - loss: 2.9531e-05 - val_loss: 2.9466e-05\n","Epoch 12/100\n"," - 175s - loss: 2.9491e-05 - val_loss: 2.9440e-05\n","Epoch 13/100\n"," - 176s - loss: 2.9443e-05 - val_loss: 2.9337e-05\n","Epoch 14/100\n"," - 175s - loss: 2.9391e-05 - val_loss: 2.9306e-05\n","Epoch 15/100\n"," - 176s - loss: 2.9343e-05 - val_loss: 2.9234e-05\n","Epoch 16/100\n"," - 175s - loss: 2.9273e-05 - val_loss: 2.9203e-05\n","Epoch 17/100\n"," - 176s - loss: 2.9156e-05 - val_loss: 2.9000e-05\n","Epoch 18/100\n"," - 175s - loss: 2.8952e-05 - val_loss: 2.8682e-05\n","Epoch 19/100\n"," - 175s - loss: 2.8447e-05 - val_loss: 2.7892e-05\n","Epoch 20/100\n"," - 176s - loss: 2.7296e-05 - val_loss: 2.6421e-05\n","Epoch 21/100\n"," - 175s - loss: 2.5689e-05 - val_loss: 2.4817e-05\n","Epoch 22/100\n"," - 176s - loss: 2.3885e-05 - val_loss: 2.2583e-05\n","Epoch 23/100\n"," - 175s - loss: 2.1045e-05 - val_loss: 1.9190e-05\n","Epoch 24/100\n"," - 176s - loss: 1.7256e-05 - val_loss: 1.5519e-05\n","Epoch 25/100\n"," - 176s - loss: 1.4705e-05 - val_loss: 1.3598e-05\n","Epoch 26/100\n"," - 178s - loss: 1.3031e-05 - val_loss: 1.2178e-05\n","Epoch 27/100\n"," - 176s - loss: 1.1808e-05 - val_loss: 1.1176e-05\n","Epoch 28/100\n"," - 176s - loss: 1.0787e-05 - val_loss: 1.0205e-05\n","Epoch 29/100\n"," - 176s - loss: 9.9380e-06 - val_loss: 9.6541e-06\n","Epoch 30/100\n"," - 176s - loss: 9.1955e-06 - val_loss: 9.0862e-06\n","Epoch 31/100\n"," - 176s - loss: 8.6513e-06 - val_loss: 8.6056e-06\n","Epoch 32/100\n"," - 175s - loss: 8.1041e-06 - val_loss: 7.7953e-06\n","Epoch 33/100\n"," - 179s - loss: 7.7103e-06 - val_loss: 7.4017e-06\n","Epoch 34/100\n"," - 180s - loss: 7.2471e-06 - val_loss: 7.0844e-06\n","Epoch 35/100\n"," - 177s - loss: 6.9071e-06 - val_loss: 6.7555e-06\n","Epoch 36/100\n"," - 176s - loss: 6.5128e-06 - val_loss: 6.2985e-06\n","Epoch 37/100\n"," - 176s - loss: 6.1748e-06 - val_loss: 5.9435e-06\n","Epoch 38/100\n"," - 176s - loss: 5.8622e-06 - val_loss: 5.8327e-06\n","Epoch 39/100\n"," - 178s - loss: 5.6284e-06 - val_loss: 5.4041e-06\n","Epoch 40/100\n"," - 177s - loss: 5.3711e-06 - val_loss: 5.2899e-06\n","Epoch 41/100\n"," - 176s - loss: 5.1821e-06 - val_loss: 5.0409e-06\n","Epoch 42/100\n"," - 176s - loss: 4.9900e-06 - val_loss: 4.9342e-06\n","Epoch 43/100\n"," - 175s - loss: 4.8199e-06 - val_loss: 4.6388e-06\n","Epoch 44/100\n"," - 177s - loss: 4.6418e-06 - val_loss: 4.4883e-06\n","Epoch 45/100\n"," - 177s - loss: 4.4740e-06 - val_loss: 4.3210e-06\n","Epoch 46/100\n"," - 176s - loss: 4.3206e-06 - val_loss: 4.1485e-06\n","Epoch 47/100\n"," - 177s - loss: 4.1455e-06 - val_loss: 4.0841e-06\n","Epoch 48/100\n"," - 177s - loss: 3.9538e-06 - val_loss: 3.7824e-06\n","Epoch 49/100\n"," - 177s - loss: 3.7662e-06 - val_loss: 3.5887e-06\n","Epoch 50/100\n"," - 176s - loss: 3.5632e-06 - val_loss: 3.4692e-06\n","Epoch 51/100\n"," - 176s - loss: 3.3570e-06 - val_loss: 3.1728e-06\n","Epoch 52/100\n"," - 177s - loss: 3.1556e-06 - val_loss: 3.0343e-06\n","Epoch 53/100\n"," - 177s - loss: 2.9549e-06 - val_loss: 2.8099e-06\n","Epoch 54/100\n"," - 176s - loss: 2.7777e-06 - val_loss: 2.6306e-06\n","Epoch 55/100\n"," - 177s - loss: 2.6142e-06 - val_loss: 2.4920e-06\n","Epoch 56/100\n"," - 177s - loss: 2.4689e-06 - val_loss: 2.3292e-06\n","Epoch 57/100\n"," - 177s - loss: 2.3222e-06 - val_loss: 2.2217e-06\n","Epoch 58/100\n"," - 177s - loss: 2.2024e-06 - val_loss: 2.0871e-06\n","Epoch 59/100\n"," - 177s - loss: 2.0851e-06 - val_loss: 1.9868e-06\n","Epoch 60/100\n"," - 177s - loss: 1.9893e-06 - val_loss: 1.9073e-06\n","Epoch 61/100\n"," - 179s - loss: 1.9092e-06 - val_loss: 1.8342e-06\n","Epoch 62/100\n"," - 177s - loss: 1.8432e-06 - val_loss: 1.7786e-06\n","Epoch 63/100\n"," - 176s - loss: 1.7958e-06 - val_loss: 1.7245e-06\n","Epoch 64/100\n"," - 177s - loss: 1.7466e-06 - val_loss: 1.6840e-06\n","Epoch 65/100\n"," - 178s - loss: 1.7173e-06 - val_loss: 1.6545e-06\n","Epoch 66/100\n"," - 177s - loss: 1.6860e-06 - val_loss: 1.6326e-06\n","Epoch 67/100\n"," - 177s - loss: 1.6614e-06 - val_loss: 1.6033e-06\n","Epoch 68/100\n"," - 177s - loss: 1.6415e-06 - val_loss: 1.6081e-06\n","Epoch 69/100\n"," - 176s - loss: 1.6239e-06 - val_loss: 1.6056e-06\n","Epoch 70/100\n"," - 179s - loss: 1.6131e-06 - val_loss: 1.5835e-06\n","Epoch 71/100\n"," - 177s - loss: 1.5940e-06 - val_loss: 1.5493e-06\n","Epoch 72/100\n"," - 177s - loss: 1.5903e-06 - val_loss: 1.5838e-06\n","Epoch 73/100\n"," - 177s - loss: 1.5737e-06 - val_loss: 1.5220e-06\n","Epoch 74/100\n"," - 177s - loss: 1.5683e-06 - val_loss: 1.5205e-06\n","Epoch 75/100\n"," - 177s - loss: 1.5616e-06 - val_loss: 1.5054e-06\n","Epoch 76/100\n"," - 177s - loss: 1.5548e-06 - val_loss: 1.5005e-06\n","Epoch 77/100\n"," - 178s - loss: 1.5437e-06 - val_loss: 1.5210e-06\n","Epoch 78/100\n"," - 176s - loss: 1.5374e-06 - val_loss: 1.4915e-06\n","Epoch 79/100\n"," - 175s - loss: 1.5333e-06 - val_loss: 1.4894e-06\n","Epoch 80/100\n"," - 178s - loss: 1.5279e-06 - val_loss: 1.4776e-06\n","Epoch 81/100\n"," - 177s - loss: 1.5305e-06 - val_loss: 1.4717e-06\n","Epoch 82/100\n"," - 177s - loss: 1.5168e-06 - val_loss: 1.5267e-06\n","Epoch 83/100\n"," - 177s - loss: 1.5136e-06 - val_loss: 1.4712e-06\n","Epoch 84/100\n"," - 176s - loss: 1.5083e-06 - val_loss: 1.4580e-06\n","Epoch 85/100\n"," - 176s - loss: 1.5069e-06 - val_loss: 1.5187e-06\n","Epoch 86/100\n"," - 178s - loss: 1.5051e-06 - val_loss: 1.4541e-06\n","Epoch 87/100\n"," - 182s - loss: 1.4994e-06 - val_loss: 1.4482e-06\n","Epoch 88/100\n"," - 182s - loss: 1.4947e-06 - val_loss: 1.4720e-06\n","Epoch 89/100\n"," - 181s - loss: 1.4943e-06 - val_loss: 1.4433e-06\n","Epoch 90/100\n"," - 181s - loss: 1.4884e-06 - val_loss: 1.4391e-06\n","Epoch 91/100\n"," - 181s - loss: 1.4865e-06 - val_loss: 1.4380e-06\n","Epoch 92/100\n"," - 180s - loss: 1.4813e-06 - val_loss: 1.4409e-06\n","Epoch 93/100\n"," - 179s - loss: 1.4827e-06 - val_loss: 1.4281e-06\n","Epoch 94/100\n"," - 177s - loss: 1.4799e-06 - val_loss: 1.4321e-06\n","Epoch 95/100\n"," - 177s - loss: 1.4710e-06 - val_loss: 1.4222e-06\n","Epoch 96/100\n"," - 178s - loss: 1.4756e-06 - val_loss: 1.4277e-06\n","Epoch 97/100\n"," - 177s - loss: 1.4701e-06 - val_loss: 1.4599e-06\n","Epoch 98/100\n"," - 178s - loss: 1.4697e-06 - val_loss: 1.4477e-06\n","Epoch 99/100\n"," - 177s - loss: 1.4640e-06 - val_loss: 1.4288e-06\n","Epoch 100/100\n"," - 176s - loss: 1.4615e-06 - val_loss: 1.4160e-06\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"-FjdXmqFjcla","colab_type":"code","outputId":"724504b0-0a0b-4b44-ab0b-691a90cb4c04","executionInfo":{"status":"error","timestamp":1588841568688,"user_tz":-330,"elapsed":3030,"user":{"displayName":"PRITAM PALIT","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Giix9Z-rAuOW8-Wch3FyW7hklDgx0X8ExfVyr5q5A=s64","userId":"02421712431687232718"}},"colab":{"base_uri":"https://localhost:8080/","height":237}},"source":["plt.plot(lstm_autoencoder_history['loss'], linewidth=2, label='Train')\n","plt.plot(lstm_autoencoder_history['val_loss'], linewidth=2, label='Valid')\n","plt.legend(loc='upper right')\n","plt.title('Model loss')\n","plt.ylabel('Loss')\n","plt.xlabel('Epoch')\n","plt.yscale(\"log\")\n","plt.show()"],"execution_count":0,"outputs":[{"output_type":"error","ename":"NameError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-1-9dc4019940c3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlstm_autoencoder_history\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'loss'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlinewidth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Train'\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      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlstm_autoencoder_history\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'val_loss'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlinewidth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Valid'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'upper right'\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[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Model loss'\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[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Loss'\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 'plt' is not defined"]}]},{"cell_type":"code","metadata":{"id":"dykiNf3ll1FF","colab_type":"code","colab":{}},"source":["X_valid_all = np.stack(df_valid['histo'].values, axis=0)\n","X_valid_all_norm=normalize(X_valid_all, norm='l1', axis=1)\n","\n","valid_x_predictions = autoencoder.predict(X_valid_all_norm)\n","#mse = np.mean(np.power(df_valid_x_rescaled - valid_x_predictions, 2), axis=1)\n","mse_valid=K.eval(mseTop10(X_valid_all_norm, valid_x_predictions))\n","error_df = pd.DataFrame({'Reconstruction_error': mse_valid,\n","                        'True_class': df_valid['labels']})\n","precision_rt, recall_rt, threshold_rt = precision_recall_curve(error_df.True_class, error_df.Reconstruction_error)\n","plt.plot(threshold_rt, precision_rt[1:], label=\"Precision\",linewidth=5)\n","plt.plot(threshold_rt, recall_rt[1:], label=\"Recall\",linewidth=5)\n","plt.title('Precision and recall for different threshold values')\n","plt.xlabel('Threshold')\n","plt.ylabel('Precision/Recall')\n","plt.legend()\n","plt.show()"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"U_5SaoSO8463","colab_type":"code","outputId":"1b9c068f-266d-4232-b895-064fb637f18d","executionInfo":{"status":"ok","timestamp":1588842647432,"user_tz":-330,"elapsed":8920,"user":{"displayName":"PRITAM PALIT","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Giix9Z-rAuOW8-Wch3FyW7hklDgx0X8ExfVyr5q5A=s64","userId":"02421712431687232718"}},"colab":{"base_uri":"https://localhost:8080/","height":521}},"source":["0#extract test and train data and put in dataframes\n","\n","#df_train=df.loc[df['labels']==True]\n","#df_test=df.loc[df['labels']==False]\n","X_trainCol = np.stack(df['histo'][df['labels']==True].values, axis=1) #convert list of array to a stack to feed the model\n","X_testCol = np.stack(df['histo'][df['labels']==False].values, axis=1)\n","X_trainS = np.stack(df['histo'][df['labels']==True].values, axis=0) #convert list of array to a stack to feed the model\n","X_testS = np.stack(df['histo'][df['labels']==False].values, axis=0)\n","\n","print(X_trainCol.shape , X_trainS.shape)\n","print(X_trainCol)\n","print(X_trainS)\n","\n","from sklearn.preprocessing import normalize\n","\n","X_train=normalize(X_trainS, norm='l1', axis=1) #normalise the sample, i.e the rows\n","X_test=normalize(X_testS, norm='l1', axis=1)\n","print(X_train)\n","                      \n","df['origin']=''\n","df['origin'].loc[df['labels']==True]=X_train.tolist()\n","df['origin'].loc[df['labels']==False]=X_test.tolist()\n","print(df.shape)\n","print(df.origin.shape)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["(100, 202299) (202299, 100)\n","[[ 33  43  34 ...  11   6   0]\n"," [213 201 241 ...  43  47   2]\n"," [426 389 362 ...  74  62   1]\n"," ...\n"," [ 54  65  43 ...   0   2   0]\n"," [ 52  74  49 ...   0   0   0]\n"," [ 67  50  55 ...   0   0   0]]\n","[[ 33 213 426 ...  54  52  67]\n"," [ 43 201 389 ...  65  74  50]\n"," [ 34 241 362 ...  43  49  55]\n"," ...\n"," [ 11  43  74 ...   0   0   0]\n"," [  6  47  62 ...   2   0   0]\n"," [  0   2   1 ...   0   0   0]]\n","[[0.0001941  0.00125283 0.00250566 ... 0.00031762 0.00030586 0.00039408]\n"," [0.00026486 0.00123807 0.00239607 ... 0.00040037 0.00045581 0.00030798]\n"," [0.00021593 0.00153059 0.00229905 ... 0.00027309 0.0003112  0.0003493 ]\n"," ...\n"," [0.00108868 0.00425574 0.00732383 ... 0.         0.         0.        ]\n"," [0.00059137 0.00463237 0.00611078 ... 0.00019712 0.         0.        ]\n"," [0.         0.00611621 0.0030581  ... 0.         0.         0.        ]]\n"],"name":"stdout"},{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py:671: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\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","  self._setitem_with_indexer(indexer, value)\n"],"name":"stderr"},{"output_type":"stream","text":["(225954, 12)\n","(225954,)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"2tcZI-bxZbEH","colab_type":"code","outputId":"b358ddb2-3664-4956-86a4-21d17332be75","executionInfo":{"status":"error","timestamp":1588842651887,"user_tz":-330,"elapsed":1596,"user":{"displayName":"PRITAM PALIT","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Giix9Z-rAuOW8-Wch3FyW7hklDgx0X8ExfVyr5q5A=s64","userId":"02421712431687232718"}},"colab":{"base_uri":"https://localhost:8080/","height":872}},"source":["color = {False:'red', True:'green'}\n","Bad=df[df['labels']==False]\n","Good=df[df['labels']==True]\n","#fraudlist = creditData[creditData.Class == 1]\n","#normal = creditData[creditData.Class == 0]\n","fig,axes = plt.subplots(1,2)\n","axes[0].scatter(list(range(1,Bad.shape[0] + 1)), X_train, color='red')\n","axes[1].scatter(list(range(1, Good.shape[0] + 1)),X_test, color='green')\n","plt.show()"],"execution_count":0,"outputs":[{"output_type":"error","ename":"ValueError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)","\u001b[0;32m<ipython-input-15-00cbdfdf7950>\u001b[0m in 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If any parameter follows {name!r}, they \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    357\u001b[0m                 f\"should be pass as keyword, not positionally.\")\n\u001b[0;32m--> 358\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    359\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    360\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mscatter\u001b[0;34m(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, edgecolors, plotnonfinite, **kwargs)\u001b[0m\n\u001b[1;32m   4378\u001b[0m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4379\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4380\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"x and y must be the same size\"\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   4381\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4382\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0ms\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mValueError\u001b[0m: x and y must be the same size"]},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 720x576 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"uwgrqBoqd59l","colab_type":"code","colab":{}},"source":["array_good=np.array(X_trainCol)\n","array_bad = np.array(X_testCol)\n","\n","mean_good=np.mean(array_good, axis=1)\n","mean_bad=np.mean(array_bad, axis=1)\n","\n","std_good=np.std(array_good, axis=1)\n","std_bad = np.std(array_bad, axis=1)\n","\n","print(mean_good.shape)\n","print(std_good.shape)\n","print(mean_good)\n","print(std_good)\n","\n","\n","#bin =[]\n","\n","#for i in range(1,101):\n","#    bin.append(i)\n","\n","#print(bin)    \n","#np.reshape(bin, (100,1))\n","#print(bin.shape)\n","bin=np.arange(1,101)\n","bin.shape\n","\n","#np.reshape(bin, (100,1))\n","#plt.plot(bin,mean_good)\n","plt.figure(1)\n","plt.errorbar(bin, mean_good, yerr=std_good, ecolor = 'green', label='both limits (default)')\n","plt.figure(2)\n","plt.errorbar(bin, mean_bad, yerr=std_bad, ecolor = 'red', label='both limits (default)')\n","plt.figure(3)\n","plt.errorbar(bin, mean_good, yerr=std_good, ecolor = 'green', label='both limits (default)')\n","plt.errorbar(bin, mean_bad, yerr=std_bad, ecolor = 'red', label='both limits (default)')\n","plt.show()\n","plt.figure(4)\n","plt.plot(bin, mean_good, color = 'green')\n","plt.plot(bin, mean_bad, color = 'red')\n","plt.figure(5)\n","plt.plot(bin, std_good, color = 'green')\n","plt.plot(bin, std_bad, color = 'red')\n","plt.show()\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"34nA2L7M8467","colab_type":"code","colab":{}},"source":["#build the model\n","import math\n","from keras.callbacks import ModelCheckpoint, EarlyStopping\n","from keras.layers import Input\n","from keras.layers.advanced_activations import PReLU\n","from keras.models import Model\n","from keras import backend as K\n","import tensorflow as tf\n","from keras.models import load_model\n","\n","def mseTop10(y_true, y_pred):\n","    top_values, _ = tf.nn.top_k(K.square(y_pred - y_true), k=10, sorted=True)\n","    mean=K.mean(top_values, axis=-1)\n","    return mean\n","\n","\n","input_size=len(X_train[0])\n","print(input_size)\n","input_layer = Input(shape=(input_size, ))\n","encoded = Dense(10, activation='tanh')(input_layer)\n","encoded1 = Dense(3, activation='tanh')(encoded)\n","encoded2 = Dense(10, activation='tanh')(encoded1)\n","decoder = Dense(input_size, activation='tanh')(encoded2)\n","\n","autoencoder = Model(inputs=input_layer, outputs=decoder)\n","autoencoder.compile(optimizer='adam', loss=mseTop10)\n","\n","for i, layer in enumerate(autoencoder.layers):\n","             layer.name = 'layer_' + str(i)\n","\n","autoencoder.summary()"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"IN0Iml9P846_","colab_type":"code","colab":{}},"source":["history = autoencoder.fit(X_train,X_train, epochs=100, batch_size=500, shuffle=False, verbose=1,validation_split=0.1)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"sNYt4TU7847E","colab_type":"code","colab":{}},"source":["#print(history)\n","\n","line_styles = [(0, ()), (0, (1, 3)), (0, (3, 2))]\n","\n","color_palette = {\"Indigo\": {\n","                    50: \"#E8EAF6\",\n","                    100: \"#C5CAE9\",\n","                    200: \"#9FA8DA\",\n","                    300: \"#7986CB\",\n","                    400: \"#5C6BC0\",\n","                    500: \"#3F51B5\",\n","                    600: \"#3949AB\",\n","                    700: \"#303F9F\",\n","                    800: \"#283593\",\n","                    900: \"#1A237E\"},\n","                 \"Teal\": {      \n","                    50: \"#E0F2F1\",\n","                    100: \"#B2DFDB\",\n","                    200: \"#80CBC4\",\n","                    300: \"#4DB6AC\",\n","                    400: \"#26A69A\",\n","                    500: \"#009688\",\n","                    600: \"#00897B\",\n","                    700: \"#00796B\",\n","                    800: \"#00695C\",\n","                    900: \"#004D40\"}\n","                }\n","\n","def plot_loss(data, title):\n","    \"\"\"Plots the training and validation loss\"\"\"\n","    plt.figure()\n","    plt.xlabel(\"Epoch\")\n","    plt.ylabel(\"Loss\")\n","    plt.title(title)\n","    plt.plot(data.history['loss'], linestyle=line_styles[0], color=color_palette[\"Indigo\"][900], linewidth=3)\n","    plt.plot(data.history['val_loss'], linestyle=line_styles[2], color=color_palette[\"Teal\"][300], linewidth=3)\n","    plt.legend([\"Train\", \"Validation\"], loc=\"upper right\", frameon=False)\n","    #plt.ylim(8258339,8258400)\n","    plt.yscale(\"log\")\n","    plt.xlim(0,100)\n","    plt.show();\n","\n","plot_loss(history, \"Original model loss\")"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"Oizgtn4YUdfU","colab_type":"code","colab":{}},"source":["#LSTM\n","from numpy import array\n","from keras.models import Sequential\n","from keras.models import Sequential\n","from keras.layers import LSTM\n","from keras.layers import Dense\n","from keras.layers import RepeatVector\n","from keras.layers import TimeDistributed\n","from keras.utils import plot_model\n","from keras.layers.advanced_activations import ReLU\n","\n","# define input sequence\n","X_train_lstm=normalize(X_trainS, norm='l1', axis=1)\n","X_test_lstm=normalize(X_testS, norm='l1', axis=1)\n","                      \n","df['lstm']=''\n","df['lstm'].loc[df['labels']==True]=X_train_lstm.tolist()\n","df['lstm'].loc[df['labels']==False]=X_test_lstm.tolist()\n","\n","#sequence = array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9])\n","#print(sequence.shape)\n","#n_in_seq = len(sequence)\n","#print(n_in_seq)\n","#sequence = sequence.reshape((1, n_in_seq, 1))\n","#print(sequence.shape)\n","\n","# reshape input into [samples, timesteps, features]\n","n_in = len(X_train_lstm[0])\n","print(n_in)\n","print(X_train_lstm.shape)\n","X_train_lstm = X_train_lstm.reshape((202299, n_in, 1))\n","print(X_train_lstm.shape)\n","# define model\n","n_in = len(X_train_lstm[0])\n","model = Sequential()\n","model.add(LSTM(10, activation='tanh', input_shape=(n_in,1)))\n","model.add(RepeatVector(n_in))\n","model.add(LSTM(10, activation='tanh', return_sequences=True))\n","model.add(TimeDistributed(Dense(1)))\n","model.compile(optimizer='adam', loss='mse')\n","for i, layer in enumerate(model.layers):\n","             layer.name = 'layer_' + str(i)\n","\n","model.summary()\n","#history_lstm = model.fit(X_train_lstm,X_train_lstm, epochs=60, batch_size=500, shuffle=False, verbose=1,validation_split=0.1)\n","# fit model\n","#model.fit(sequence, sequence, epochs=300, verbose=0)\n","#plot_model(model, show_shapes=True, to_file='reconstruct_lstm_autoencoder.png')"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"DVcW28RruHaf","colab_type":"code","colab":{}},"source":["#print(history)\n","\n","line_styles = [(0, ()), (0, (1, 3)), (0, (3, 2))]\n","\n","color_palette = {\"Indigo\": {\n","                    50: \"#E8EAF6\",\n","                    100: \"#C5CAE9\",\n","                    200: \"#9FA8DA\",\n","                    300: \"#7986CB\",\n","                    400: \"#5C6BC0\",\n","                    500: \"#3F51B5\",\n","                    600: \"#3949AB\",\n","                    700: \"#303F9F\",\n","                    800: \"#283593\",\n","                    900: \"#1A237E\"},\n","                 \"Teal\": {      \n","                    50: \"#E0F2F1\",\n","                    100: \"#B2DFDB\",\n","                    200: \"#80CBC4\",\n","                    300: \"#4DB6AC\",\n","                    400: \"#26A69A\",\n","                    500: \"#009688\",\n","                    600: \"#00897B\",\n","                    700: \"#00796B\",\n","                    800: \"#00695C\",\n","                    900: \"#004D40\"}\n","                }\n","\n","def plot_loss(data, title):\n","    \"\"\"Plots the training and validation loss\"\"\"\n","    plt.figure()\n","    plt.xlabel(\"Epoch\")\n","    plt.ylabel(\"Loss\")\n","    plt.title(title)\n","    plt.plot(data.history['loss'], linestyle=line_styles[0], color=color_palette[\"Indigo\"][900], linewidth=3)\n","    plt.plot(data.history['val_loss'], linestyle=line_styles[2], color=color_palette[\"Teal\"][300], linewidth=3)\n","    plt.legend([\"Train\", \"Validation\"], loc=\"upper right\", frameon=False)\n","    #plt.ylim(8258339,8258400)\n","    plt.yscale(\"log\")\n","    plt.xlim(0,60)\n","    plt.show();\n","\n","plot_loss(history_lstm, \"Original model loss for LSTM\")"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"nMwfYomB847I","colab_type":"code","colab":{}},"source":["predictionTest=autoencoder.predict(X_test)\n","predictionTrain=autoencoder.predict(X_train)\n","\n","mseTest=K.eval(mseTop10(X_test, predictionTest))\n","mseTrain=K.eval(mseTop10(X_train, predictionTrain))\n","\n","df['prediction']=\"\"\n","df['mse']=\"\"\n","\n","df['prediction'].loc[df['labels']==True]=predictionTrain.tolist()\n","df['prediction'].loc[df['labels']==False]=predictionTest.tolist()\n","\n","df['mse'].loc[df['labels']==True]=mseTrain\n","df['mse'].loc[df['labels']==False]=mseTest\n","\n","print(df.head())\n","#print(df_test.head())\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"dwfnA8aT847N","colab_type":"code","colab":{}},"source":["def globalMSETrend(df,type=1):\n","    \n","    y=[]\n","    \n","    rmax=0\n","    rmin=10\n","    \n","    print(rmax, rmin)\n","    for run in df['fromrun'].unique():\n","        if (type==1): val=(df['mse'][run]).mean()\n","        if (type==2): val=(df['mse2'][run]).mean()\n","        if val > rmax: rmax=val\n","        if val < rmin: rmin =val\n","        y.append(val)\n","    \n","    array=np.array(y)\n","    gmean=array.mean()\n","    size=float(array.size)\n","    gstd=array.std()\n","    print(size)\n","    \n","    fig=plt.figure()\n","    fig.set_size_inches(16, 9)\n","    \n","    plt.hlines(gmean,df['fromrun'].unique().min(), df['fromrun'].unique().max(), color=\"blue\", label=\"Run average: \" + str(gmean))\n","    plt.hlines(gmean+(1.0*gstd), df['fromrun'].unique().min(), df['fromrun'].unique().max(), color=\"red\", label='1 SD (' + str(gstd) + \")\")\n","    plt.hlines(gmean+(3.0*gstd), df['fromrun'].unique().min(), df['fromrun'].unique().max(), color=\"red\", label='3 SD', linestyle=':')\n","    \n","    plt.ylim(rmin*0.9,rmax*1.1)\n","    plt.scatter(df['fromrun'].unique(), y, marker='+', label='Data points')\n","    plt.xlabel(\"Run\")\n","    plt.ylabel(\"average MSE\")\n","    plt.legend()\n","    plt.show()"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"vXTdvYe-847Y","colab_type":"code","colab":{}},"source":["globalMSETrend(df[df.labels==True])"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"RUTWmHU9847f","colab_type":"code","colab":{}},"source":["def CheckPredictions(df_test, run, ls):\n","\n","    Xmax=80000.0\n","    Xmin=0.0\n","    Xbins=100\n","    hname=\"chargeInner_PXLayer_1\"\n","    \n","    ahisto=df_test['origin'][run][ls]\n","    ahisto1=df_test['prediction'][run][ls]\n","    mse=df_test['mse'][run][ls]\n","    \n","    x= np.linspace(Xmin,Xmax,Xbins)\n","    #plt.xlim(Xmin,Xmax)\n","    \n","    plt.step(x, ahisto, where='mid', label=(\" Data \" + str(df_test.fromlumi[run][ls]) + \" Run \" + str(df_test.fromrun[run][ls]) ))\n","    plt.step(x, ahisto1, where='mid', label=\" Reco MSE=\" + str(mse))\n","    plt.xlabel(\"Charge electrons\")\n","    plt.ylabel(\"A.U\")\n","    plt.legend()\n","    plt.show()"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"AegPXiM2847l","colab_type":"code","colab":{}},"source":["for run in df['fromrun'].unique():\n","    count=0\n","    for ls in df['fromlumi'][run]: \n","        if df['labels'][run][ls]== False: continue\n","        if (count >2): continue\n","        count=count+1\n","        #CheckPredictions(df[df.train==True], run,ls)\n","        CheckPredictions(df[df['labels']==True], run,ls)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"7I2cEQhh847z","colab_type":"code","colab":{}},"source":["import pandas as pd\n","import numpy as np\n","#X_trainA = np.stack(df['origin'][df['labels']==True].values, axis=0) #convert list of array to a stack to feed the model\n","X_trainA = np.stack(df['origin'][df['labels']==True].values, axis=0)\n","#no need to normalize, we already have the normalized histogram in the df\n","print(X_trainA)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"utxmUKwQ8477","colab_type":"code","colab":{}},"source":["input_size=len(X_train[0])\n","input_layerA = Input(shape=(input_size, ))\n","\n","encodedA = Dense(20, activation='sigmoid')(input_layerA)\n","encoded1A = Dense(10, activation='tanh')(encodedA)\n","encoded2A = Dense(20, activation='sigmoid')(encoded1A)\n","decoderA = Dense(input_size, activation='sigmoid')(encoded2A)\n","\n","autoencoder_Opt = Model(inputs=input_layerA, outputs=decoderA)\n","autoencoder_Opt.compile(optimizer='adam', loss=mseTop10)\n","\n","for i, layer in enumerate(autoencoder_Opt.layers):\n","             layer.name = 'layer_' + str(i)\n","\n","autoencoder_Opt.summary()"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"D6U5jfWG848B","colab_type":"code","colab":{}},"source":["history_Opt = autoencoder_Opt.fit(X_trainA,X_trainA, epochs=300, batch_size=100, shuffle=True, verbose=1,validation_split=0.1)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"GasWyIYt848H","colab_type":"code","colab":{}},"source":["#calculate predictions and mse and add to original DF\n","\n","originS=np.stack(df['origin'].values, axis=0)\n","predictionA=autoencoder_Opt.predict(originS)\n","\n","mseA=K.eval(mseTop10(originS,predictionA))\n","\n","df['predictions2']=\"\"\n","df['mse2']=\"\"\n","\n","df['prediction2']=predictionA.tolist()\n","df['mse2']=mseA\n","\n","\n","print(df.head())"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"8fV2lG9G848O","colab_type":"code","colab":{}},"source":["def CheckAllPredictions(df_test, run, ls):\n","\n","    Xmax=80000.0\n","    Xmin=0.0\n","    Xbins=100\n","    hname=\"chargeInner_PXLayer_1\"\n","    \n","    ahisto=df_test['origin'][run][ls]\n","    ahisto1=df_test['prediction'][run][ls]\n","    ahisto2=df_test['prediction2'][run][ls]\n","    mse=df_test['mse'][run][ls]\n","    mse2=df_test['mse2'][run][ls]\n","    \n","    x= np.linspace(Xmin,Xmax,Xbins)\n","    #plt.xlim(Xmin,Xmax)\n","    \n","    plt.step(x, ahisto, where='mid', label=(\" Data \" + str(df_test.fromlumi[run][ls]) + \" Run \" + str(df_test.fromrun[run][ls]) ))\n","    plt.step(x, ahisto1, where='mid', label=\" Raw Reco, mse:\" + str(mse))\n","    plt.step(x, ahisto2, where='mid', label=\" Reco, mse:\" + str(mse2))\n","    plt.xlabel(\"Charge electrons\")\n","    plt.ylabel(\"A.U\")\n","    plt.legend()\n","    plt.show()"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"_FP5IaCJ848U","colab_type":"code","colab":{}},"source":["#compare some old and new predictions\n","\n","for run in df['fromrun'].unique():\n","    count=0\n","    if run <298000: continue\n","    for ls in df['fromlumi'][run]: \n","        if df['train'][run][ls]== True: continue\n","        if df['mse2'][run][ls]<0.00001: continue\n","        if df['entries'][run][ls]<3000: continue\n","        #if (count >2): continue\n","        count=count+1\n","        #print(df.entries)[run][ls]\n","        CheckAllPredictions(df[df.train==False], run,ls)"],"execution_count":0,"outputs":[]}]}