{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load in \n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv\nfrom keras.models import model_from_json\n\nimport os\nprint(os.listdir(\"../input\"))\n\n# Any results you write to the current directory are saved as output.","execution_count":162,"outputs":[{"output_type":"stream","text":"['test', 'sample_submission.csv', 'train.csv']\n","name":"stdout"}]},{"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","trusted":true},"cell_type":"code","source":"import random\nimport sys\nimport io\nimport os\nimport glob\nimport IPython\nimport matplotlib.pyplot as plt\nimport gc\nimport keras\nfrom keras.callbacks import ModelCheckpoint\nfrom keras.models import Model, load_model, Sequential\nfrom keras.layers import Dense, Activation, Dropout, Input, Masking, TimeDistributed, LSTM, Conv1D, Conv2D, Conv3D\nfrom keras.layers import GRU, Bidirectional, BatchNormalization, Reshape\nfrom keras.optimizers import Adam","execution_count":163,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sample_data=True\nsample_per=100","execution_count":164,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"71c75301d09d58cf412d580d789c52c0eef29912"},"cell_type":"code","source":"#this one is high def data\n\n#%%time\nrowcount = 1000000000\n    \nif(not sample_data):\n    #kernel supports >6m <60m \n    train = pd.read_csv(\"../input/train.csv\", nrows=rowcount) \n    train.rename({\"acoustic_data\": \"acd\", \"time_to_failure\": \"ttf\"}, axis=\"columns\", inplace=True)\n\n    acd=train['acd']\n    ttf=train['ttf']\n\n\nif(sample_data):\n    train = pd.read_csv('../input/train.csv', dtype={'acoustic_data': np.int16, 'time_to_failure': np.float32})\n    train.rename({\"acoustic_data\": \"acd\", \"time_to_failure\": \"ttf\"}, axis=\"columns\", inplace=True)\n    acd_small = train['acd'].values[::sample_per]\n    ttf_small = train['ttf'].values[::sample_per]\n    acd=acd_small\n    ttf=ttf_small\n    \n    rowcount=int(train.shape[0]/sample_per)\n    \n#CHANGE THIS TO ROW BY ROW READING, TO GET ALL THE DATA AT ONCE. USE PYTHON READ CSV\n#https://docs.python.org/2/library/csv.html","execution_count":165,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(rowcount,\" \",train[\"acd\"].size,\" \",train[\"ttf\"].size)","execution_count":166,"outputs":[{"output_type":"stream","text":"6291454   629145480   629145480\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"plt.plot(ttf)","execution_count":167,"outputs":[{"output_type":"execute_result","execution_count":167,"data":{"text/plain":"[<matplotlib.lines.Line2D at 0x7f954122c710>]"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"def feature_generate(df,x,seg):\n\n    for windows in [10, 100, 1000]:\n        x_roll_std = x.rolling(windows).std().dropna().values\n        df.loc[seg, '5q_roll_std' + str(windows)] = np.quantile(x_roll_std, 0.05)\n        \n    windows = 10\n    x_roll_std = x.rolling(windows).std().dropna().values\n    df.loc[seg, 'av_change_abs_roll_std' + str(windows)] = np.mean(np.diff(x_roll_std))\n    \n    windows = 100\n    x_roll_mean = x.rolling(windows).mean().dropna().values\n    df.loc[seg, 'std_roll_mean' + str(windows)] = x_roll_mean.std()\n    \n    return df","execution_count":168,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"segments = int(np.floor(train.shape[0] / 150000))","execution_count":169,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X_train = pd.DataFrame(index=range(segments), dtype=np.float64)\ny_train = pd.DataFrame(index=range(segments), dtype=np.float64, columns=['ttf'])","execution_count":170,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"for s in range(segments):\n    seg = train.iloc[s*150000:s*150000+150000]\n    x = pd.Series(seg['acd'].values)\n    y = seg['ttf'].values[-1]\n    y_train.loc[s, 'ttf'] = y\n    X_train = feature_generate(X_train,x,s)\ncolumns=X_train.columns  \ndel train\ngc.collect()\nX_train[\"5q_roll_std10\"].size","execution_count":171,"outputs":[{"output_type":"execute_result","execution_count":171,"data":{"text/plain":"4194"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"X_train.shape","execution_count":172,"outputs":[{"output_type":"execute_result","execution_count":172,"data":{"text/plain":"(4194, 5)"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"X_train.rename({\"5q_roll_std10\": \"std10\", \"5q_roll_std100\": \"std100\", \"5q_roll_std1000\": \"std1000\", \"av_change_abs_roll_std10\": \"avabsmean10\", \"std_roll_mean100\": \"stdmean100\"}, axis=\"columns\", inplace=True)\n\nstd10=X_train['std10']\nstd100=X_train['std100']\nstd1000=X_train['std1000']\navabsmean10=X_train['avabsmean10']\nstdmean100=X_train['stdmean100']\n\nprint(X_train.size, y_train.size)","execution_count":173,"outputs":[{"output_type":"stream","text":"20970 4194\n","name":"stdout"}]},{"metadata":{"trusted":true,"_uuid":"a78d6f076f42f6c8a397455d08b4800d5dc6acad"},"cell_type":"code","source":"# GRADED FUNCTION: model\n\ndef model(input_shape):\n    \"\"\"\n    Function creating the model's graph in Keras.\n    \n    Argument:\n    input_shape -- shape of the model's input data (using Keras conventions)\n\n    Returns:\n    model -- Keras model instance\n    \"\"\"\n    \n    X_input = Input(shape = input_shape)\n    \n    ### START CODE HERE ###\n    \n    # Step 1: CONV layer (≈4 lines)\n    X = Conv1D(196, kernel_size=1, strides=1)(X_input)                                 # CONV1D\n    X = BatchNormalization()(X)                                 # Batch normalization\n    X = Activation('relu')(X)                                 # ReLu activation\n    X = Dropout(0.8)(X)                                 # dropout (use 0.8)\n\n    # Step 2: First GRU Layer (≈4 lines)\n    X = GRU(units = 128, return_sequences = True)(X) # GRU (use 128 units and return the sequences)\n    X = Dropout(0.8)(X)                                 # dropout (use 0.8)\n    X = BatchNormalization()(X)                                 # Batch normalization\n    \n    # Step 3: Second GRU Layer (≈4 lines)\n    X = GRU(units = 128, return_sequences = True)(X)   # GRU (use 128 units and return the sequences)\n    X = Dropout(0.8)(X)                                 # dropout (use 0.8)\n    X = BatchNormalization()(X)                                  # Batch normalization\n    X = Dropout(0.8)(X)                                  # dropout (use 0.8)\n    \n    # Step 4: Time-distributed dense layer (≈1 line)\n    X = TimeDistributed(Dense(1, activation = \"relu\"))(X) # time distributed\n    #X = TimeDistributed(Dense(1),activation=\"sigmoid\")(X) # time distributed  (sigmoid)\n    \n    \n    ### END CODE HERE ###\n\n    model = Model(inputs = X_input, outputs = X)\n    \n    return model\n\n#sauce: https://github.com/Gurupradeep/deeplearning.ai-Assignments/blob/master/Sequence%20Models/Week3/Trigger%2Bword%2Bdetection%2B-%2Bv1.ipynb","execution_count":251,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"0dd85c27f42618d47dce363bdcc2ed33f6ed164f"},"cell_type":"code","source":"#prepare x\nnfft = 16 # Length of each window segment\nfs = 60 # Sampling frequencies\nnoverlap = 15 # Overlap between windows\nstd10_pxx, freqs, bins, im = plt.specgram(std10[:rowcount], nfft, fs, noverlap = noverlap)\n\nsamplecount_x=std10_pxx[0].shape[0]\nprint(std10_pxx.shape)","execution_count":252,"outputs":[{"output_type":"stream","text":"(9, 4179)\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"std100_pxx, freqs, bins, im = plt.specgram(std100[:rowcount], nfft, fs, noverlap = noverlap)","execution_count":253,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"std1000_pxx, freqs, bins, im = plt.specgram(std1000[:rowcount], nfft, fs, noverlap = noverlap)","execution_count":254,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"avabsmean10_pxx, freqs, bins, im = plt.specgram(avabsmean10[:rowcount], nfft, fs, noverlap = noverlap)","execution_count":255,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"stdmean100_pxx, freqs, bins, im = plt.specgram(stdmean100[:rowcount], nfft, fs, noverlap = noverlap)","execution_count":256,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"numof_features=3\nn_freq = std10_pxx.shape[0]*numof_features\nmodel = model(input_shape = (samplecount_x, n_freq))\n\nopt = Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, decay=0.01)\nmodel.compile(loss='mse', optimizer=opt, metrics=[\"mae\"])","execution_count":257,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"avabsmean10_pxx = np.array(avabsmean10_pxx) * 10000000000\nstd10_pxx[3:] *= 10000\n#std10_pxx       = np.array(std10_pxx) * 10000\n","execution_count":258,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"std10_pxx = std10_pxx.swapaxes(0,1)\navabsmean10_pxx = avabsmean10_pxx.swapaxes(0,1)\nstdmean100_pxx = stdmean100_pxx.swapaxes(0,1)\n#numof_features * feature\n#x = np.hstack((std10_pxx, avabsmean10_pxx, stdmean100_pxx))\nx = np.hstack((std10_pxx, stdmean100_pxx, avabsmean10_pxx))\n#u = np.reshape(x,(x.shape[1],x.shape[2]))\n#x=x.swapaxes(0,1)\nu=x\nx = np.expand_dims(x, axis=0)\nprint(u.shape)\n","execution_count":259,"outputs":[{"output_type":"stream","text":"(4179, 27)\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"plt.imshow(u, cmap='jet',\n           vmin=0, vmax=1, origin='lowest', aspect='auto')\nplt.colorbar()\nplt.show()","execution_count":260,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 2 Axes>","image/png":"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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"import math\nsamplecount_y= model.predict(x).shape[1]\nsampler = int(math.ceil(rowcount/samplecount_y))\nprint(sampler)\ny_u = np.resize(y_train, (samplecount_y,1))                                                        #ONLY APPLY IF samplecount_y ~ y_train.shape[1]\n#y_u = y_train\nprint(samplecount_y , \" \" , y_train.shape, \" \", sampler, \" \", X_train.shape,\" \", y_u.shape)\nplt.imshow(y_u, cmap='jet',\n           vmin=0, vmax=20, origin='lowest', aspect='auto')\nplt.colorbar()\nplt.show()","execution_count":261,"outputs":[{"output_type":"stream","text":"1506\n4179   (4194, 1)   1506   (4194, 5)   (4179, 1)\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 2 Axes>","image/png":"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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"y_u = np.expand_dims(y_u, axis=0)","execution_count":262,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"c=0","execution_count":263,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#train\nimport time\ntimeout = time.time() + 1800\nwhile True:\n    c+=1\n    model.fit(x,y_u, epochs=1)\n    if(time.time()> timeout):\n        print(\"{0} EPOCHS\".format(c))\n        break","execution_count":265,"outputs":[{"output_type":"stream","text":"Epoch 1/1\n1/1 [==============================] - 5s 5s/step - loss: 22.4375 - mean_absolute_error: 3.6988\nEpoch 1/1\n1/1 [==============================] - 5s 5s/step - loss: 21.7960 - mean_absolute_error: 3.6534\nEpoch 1/1\n","name":"stdout"},{"output_type":"error","ename":"KeyboardInterrupt","evalue":"","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m<ipython-input-265-ea4802d5aebc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mc\u001b[0m\u001b[0;34m+=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m     \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_u\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m     \u001b[0;32mif\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m>\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"{0} EPOCHS\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.6/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)\u001b[0m\n\u001b[1;32m   1037\u001b[0m                                         \u001b[0minitial_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitial_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1038\u001b[0m                                         \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1039\u001b[0;31m                                         validation_steps=validation_steps)\n\u001b[0m\u001b[1;32m   1040\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1041\u001b[0m     def evaluate(self, x=None, y=None,\n","\u001b[0;32m/opt/conda/lib/python3.6/site-packages/keras/engine/training_arrays.py\u001b[0m in \u001b[0;36mfit_loop\u001b[0;34m(model, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch, steps_per_epoch, validation_steps)\u001b[0m\n\u001b[1;32m    197\u001b[0m                     \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m 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tf_session.TF_SessionRunCallable(\n\u001b[1;32m   1438\u001b[0m               \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1439\u001b[0;31m               run_metadata_ptr)\n\u001b[0m\u001b[1;32m   1440\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1441\u001b[0m           \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "]}]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(\"{0} EPOCHS\".format(c))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.6.6","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":1}