{"cells":[{"metadata":{"papermill":{"duration":0.018459,"end_time":"2020-09-21T09:16:41.239793","exception":false,"start_time":"2020-09-21T09:16:41.221334","status":"completed"},"tags":[]},"cell_type":"markdown","source":"This notebook will demonstrate feature engineering and augmentation for the GRU/LSTM model.\n\nGRU/LSTM part is mainly based on [OpenVaccine: Simple GRU Model](https://www.kaggle.com/xhlulu/openvaccine-simple-gru-model).\n"},{"metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","execution":{"iopub.execute_input":"2020-09-21T09:16:41.283808Z","iopub.status.busy":"2020-09-21T09:16:41.282968Z","iopub.status.idle":"2020-09-21T09:16:49.375397Z","shell.execute_reply":"2020-09-21T09:16:49.37468Z"},"papermill":{"duration":8.118687,"end_time":"2020-09-21T09:16:49.375551","exception":false,"start_time":"2020-09-21T09:16:41.256864","status":"completed"},"tags":[],"trusted":true},"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nimport json\nimport tensorflow.keras.layers as L\nimport keras.backend as K\nimport tensorflow as tf\nimport plotly.express as px\nfrom sklearn.model_selection import StratifiedKFold, KFold, GroupKFold\nfrom sklearn.cluster import KMeans\nimport os\n\nos.environ['CUDA_VISIBLE_DEVICES'] = '0'\ndef allocate_gpu_memory(gpu_number=0):\n    physical_devices = tf.config.experimental.list_physical_devices('GPU')\n\n    if physical_devices:\n        try:\n            print(\"Found {} GPU(s)\".format(len(physical_devices)))\n            tf.config.set_visible_devices(physical_devices[gpu_number], 'GPU')\n            tf.config.experimental.set_memory_growth(physical_devices[gpu_number], True)\n            print(\"#{} GPU memory is allocated\".format(gpu_number))\n        except RuntimeError as e:\n            print(e)\n    else:\n        print(\"Not enough GPU hardware devices available\")\nallocate_gpu_memory()\n\nVer='GRU_LSTM1'\naug_data = '../input/openvaccine-augmentation-data/aug_data1.csv'\ndebug = False","execution_count":12,"outputs":[{"output_type":"stream","text":"Found 1 GPU(s)\n#0 GPU memory is allocated\n","name":"stdout"}]},{"metadata":{"execution":{"iopub.execute_input":"2020-09-21T09:16:49.43636Z","iopub.status.busy":"2020-09-21T09:16:49.435576Z","iopub.status.idle":"2020-09-21T09:16:49.439099Z","shell.execute_reply":"2020-09-21T09:16:49.439656Z"},"papermill":{"duration":0.044,"end_time":"2020-09-21T09:16:49.439787","exception":false,"start_time":"2020-09-21T09:16:49.395787","status":"completed"},"tags":[],"trusted":true},"cell_type":"code","source":"def gru_layer(hidden_dim, dropout):\n    return L.Bidirectional(L.GRU(hidden_dim, dropout=dropout, return_sequences=True, kernel_initializer = 'orthogonal'))\n\ndef lstm_layer(hidden_dim, dropout):\n    return L.Bidirectional(L.LSTM(hidden_dim, dropout=dropout, return_sequences=True, kernel_initializer = 'orthogonal'))\n\ndef build_model(seq_len=107, pred_len=68, dropout=0.5, embed_dim=100, hidden_dim=256, type=0):\n    inputs = L.Input(shape=(seq_len, 6))\n    \n    # split categorical and numerical features and concatenate them later.\n    categorical_feat_dim = 3\n    categorical_fea = inputs[:, :, :categorical_feat_dim]\n    numerical_fea = inputs[:, :, 3:]\n\n    embed = L.Embedding(input_dim=len(token2int), output_dim=embed_dim)(categorical_fea)\n    reshaped = tf.reshape(embed, shape=(-1, embed.shape[1],  embed.shape[2] * embed.shape[3]))\n    reshaped = L.concatenate([reshaped, numerical_fea], axis=2)\n    \n    if type == 0:\n        hidden = gru_layer(hidden_dim, dropout)(reshaped)\n        hidden = gru_layer(hidden_dim, dropout)(hidden)\n        hidden = gru_layer(hidden_dim, dropout)(hidden)\n    elif type == 1:\n        hidden = gru_layer(hidden_dim, dropout)(reshaped)\n        hidden = gru_layer(hidden_dim, dropout)(hidden)\n        hidden = lstm_layer(hidden_dim, dropout)(hidden)\n    #elif type == 2:\n    #    hidden = gru_layer(hidden_dim, dropout)(reshaped)\n    #    hidden = lstm_layer(hidden_dim, dropout)(hidden)\n    #    hidden = gru_layer(hidden_dim, dropout)(hidden)\n    elif type == 2:\n        hidden = lstm_layer(hidden_dim, dropout)(reshaped)\n        hidden = lstm_layer(hidden_dim, dropout)(hidden)\n        hidden = lstm_layer(hidden_dim, dropout)(hidden)\n    \n    truncated = hidden[:, :pred_len]\n    out = L.Dense(5, activation='linear')(truncated)\n    model = tf.keras.Model(inputs=inputs, outputs=out)\n    model.compile(tf.keras.optimizers.Adam(), loss=mcrmse)\n    return model","execution_count":13,"outputs":[]},{"metadata":{"execution":{"iopub.execute_input":"2020-09-21T09:16:49.489942Z","iopub.status.busy":"2020-09-21T09:16:49.488971Z","iopub.status.idle":"2020-09-21T09:16:49.492271Z","shell.execute_reply":"2020-09-21T09:16:49.492951Z"},"papermill":{"duration":0.035182,"end_time":"2020-09-21T09:16:49.493068","exception":false,"start_time":"2020-09-21T09:16:49.457886","status":"completed"},"tags":[],"trusted":true},"cell_type":"code","source":"token2int = {x:i for i, x in enumerate('().ACGUBEHIMSX')}\npred_cols = ['reactivity', 'deg_Mg_pH10', 'deg_pH10', 'deg_Mg_50C', 'deg_50C']\n\ndef preprocess_inputs(df, cols=['sequence', 'structure', 'predicted_loop_type']):\n    base_fea = np.transpose(\n        np.array(\n            df[cols]\n            .applymap(lambda seq: [token2int[x] for x in seq])\n            .values\n            .tolist()\n        ),\n        (0, 2, 1)\n    )\n    bpps_sum_fea = np.array(df['bpps_sum'].to_list())[:,:,np.newaxis]\n    bpps_max_fea = np.array(df['bpps_max'].to_list())[:,:,np.newaxis]\n    bpps_nb_fea = np.array(df['bpps_nb'].to_list())[:,:,np.newaxis]\n    return np.concatenate([base_fea,bpps_sum_fea,bpps_max_fea,bpps_nb_fea], 2)\n\ndef rmse(y_actual, y_pred):\n    mse = tf.keras.losses.mean_squared_error(y_actual, y_pred)\n    return K.sqrt(mse)\n\ndef mcrmse(y_actual, y_pred, num_scored=len(pred_cols)):\n    score = 0\n    for i in range(num_scored):\n        score += rmse(y_actual[:, :, i], y_pred[:, :, i]) / num_scored\n    return score","execution_count":14,"outputs":[]},{"metadata":{"papermill":{"duration":0.017475,"end_time":"2020-09-21T09:16:49.527343","exception":false,"start_time":"2020-09-21T09:16:49.509868","status":"completed"},"tags":[]},"cell_type":"markdown","source":"## Load and preprocess data"},{"metadata":{"execution":{"iopub.execute_input":"2020-09-21T09:16:49.566559Z","iopub.status.busy":"2020-09-21T09:16:49.565762Z","iopub.status.idle":"2020-09-21T09:16:50.291832Z","shell.execute_reply":"2020-09-21T09:16:50.290255Z"},"lines_to_next_cell":2,"papermill":{"duration":0.747182,"end_time":"2020-09-21T09:16:50.291948","exception":false,"start_time":"2020-09-21T09:16:49.544766","status":"completed"},"tags":[],"trusted":true},"cell_type":"code","source":"train = pd.read_json('../input/stanford-covid-vaccine/train.json', lines=True)\ntest = pd.read_json('../input/stanford-covid-vaccine/test.json', lines=True)","execution_count":15,"outputs":[]},{"metadata":{"execution":{"iopub.execute_input":"2020-09-21T09:16:50.34417Z","iopub.status.busy":"2020-09-21T09:16:50.334188Z","iopub.status.idle":"2020-09-21T09:17:11.178477Z","shell.execute_reply":"2020-09-21T09:17:11.179708Z"},"papermill":{"duration":20.870377,"end_time":"2020-09-21T09:17:11.17996","exception":false,"start_time":"2020-09-21T09:16:50.309583","status":"completed"},"tags":[],"trusted":true},"cell_type":"code","source":"# additional features\n\ndef read_bpps_sum(df):\n    bpps_arr = []\n    for mol_id in df.id.to_list():\n        bpps_arr.append(np.load(f\"../input/stanford-covid-vaccine/bpps/{mol_id}.npy\").max(axis=1))\n    return bpps_arr\n\ndef read_bpps_max(df):\n    bpps_arr = []\n    for mol_id in df.id.to_list():\n        bpps_arr.append(np.load(f\"../input/stanford-covid-vaccine/bpps/{mol_id}.npy\").sum(axis=1))\n    return bpps_arr\n\ndef read_bpps_nb(df):\n    # normalized non-zero number\n    # from https://www.kaggle.com/symyksr/openvaccine-deepergcn \n    bpps_nb_mean = 0.077522 # mean of bpps_nb across all training data\n    bpps_nb_std = 0.08914   # std of bpps_nb across all training data\n    bpps_arr = []\n    for mol_id in df.id.to_list():\n        bpps = np.load(f\"../input/stanford-covid-vaccine/bpps/{mol_id}.npy\")\n        bpps_nb = (bpps > 0).sum(axis=0) / bpps.shape[0]\n        bpps_nb = (bpps_nb - bpps_nb_mean) / bpps_nb_std\n        bpps_arr.append(bpps_nb)\n    return bpps_arr \n\ntrain['bpps_sum'] = read_bpps_sum(train)\ntest['bpps_sum'] = read_bpps_sum(test)\ntrain['bpps_max'] = read_bpps_max(train)\ntest['bpps_max'] = read_bpps_max(test)\ntrain['bpps_nb'] = read_bpps_nb(train)\ntest['bpps_nb'] = read_bpps_nb(test)","execution_count":16,"outputs":[]},{"metadata":{"execution":{"iopub.execute_input":"2020-09-21T09:17:11.242395Z","iopub.status.busy":"2020-09-21T09:17:11.241357Z","iopub.status.idle":"2020-09-21T09:17:14.830513Z","shell.execute_reply":"2020-09-21T09:17:14.831555Z"},"papermill":{"duration":3.626176,"end_time":"2020-09-21T09:17:14.831719","exception":false,"start_time":"2020-09-21T09:17:11.205543","status":"completed"},"tags":[],"trusted":true},"cell_type":"code","source":"# clustering for GroupKFold\n# expecting more accurate CV by putting similar RNAs into the same fold.\nkmeans_model = KMeans(n_clusters=200, random_state=110).fit(preprocess_inputs(train)[:,:,0])\ntrain['cluster_id'] = kmeans_model.labels_","execution_count":17,"outputs":[]},{"metadata":{"papermill":{"duration":0.020176,"end_time":"2020-09-21T09:17:14.872725","exception":false,"start_time":"2020-09-21T09:17:14.852549","status":"completed"},"tags":[]},"cell_type":"markdown","source":"## Data augmentation for training and TTA(test)"},{"metadata":{"execution":{"iopub.execute_input":"2020-09-21T09:17:14.936809Z","iopub.status.busy":"2020-09-21T09:17:14.935969Z","iopub.status.idle":"2020-09-21T09:17:15.021897Z","shell.execute_reply":"2020-09-21T09:17:15.023027Z"},"papermill":{"duration":0.120191,"end_time":"2020-09-21T09:17:15.023193","exception":false,"start_time":"2020-09-21T09:17:14.903002","status":"completed"},"tags":[],"trusted":true},"cell_type":"code","source":"aug_data = '../input/openvaccineaugmentationdata/aug_data1.csv'\naug_df = pd.read_csv(aug_data)\ndisplay(aug_df.head())","execution_count":18,"outputs":[{"output_type":"display_data","data":{"text/plain":"             id                                           sequence  \\\n0  id_fff546103  GGAAAGCUAGGACGUGGGAGCGUAGCUCUCCACACGGGUACGCCAA...   \n1  id_18ff9d670  GGAAAGAGCUCGUGAGAAGAAUCUAGUACAUGCAUACGCUACAUCU...   \n2  id_177cd630b  GGAAAGAAGUAGCACGGUCCUAAGGUUACUGUAGCUAUGUCCAGCG...   \n3  id_17a9ad5b7  GGAAAACACUGCAAAAGUCAACGAAGAAGUUGACUAAGAAGUGAUC...   \n4  id_17ab91518  GGAAAACGCGCGCGCGCGCGCGCGCGCGCGCGCGCGCGCGCGCGCG...   \n\n                                           structure  log_gamma     score  \\\n0  .....((((((((((((((((...)))).)))).((((((((((.....          2  0.981885   \n1  .....(((.((.......((..((((.....((....)).....))...          0  0.887485   \n2  (....((.((((((((((((...))..))))).))))).))(((.(...          2  0.923722   \n3  ......((((((...(((((((......))))))).....((((((...          2  0.977602   \n4  ......(((((((((((((((((((((((((((((....)))))))...          2  0.982851   \n\n   cnt                                predicted_loop_type  \n0    3  EEEEESSSSSSSSSSSSSSSSHHHSSSSBSSSSMSSSSSSSSSSHH...  \n1    5  EEEEESSSISSIIIIIIISSIISSSSIIIIISSHHHHSSIIIIISS...  \n2    3  SMMMMSSISSSSSSSSSSSSHHHSSBBSSSSSBSSSSSISSSSSIS...  \n3    3  EEEEEESSSSSSMMMSSSSSSSHHHHHHSSSSSSSMMMMMSSSSSS...  \n4    3  EEEEEESSSSSSSSSSSSSSSSSSSSSSSSSSSSSHHHHSSSSSSS...  ","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>id</th>\n      <th>sequence</th>\n      <th>structure</th>\n      <th>log_gamma</th>\n      <th>score</th>\n      <th>cnt</th>\n      <th>predicted_loop_type</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>id_fff546103</td>\n      <td>GGAAAGCUAGGACGUGGGAGCGUAGCUCUCCACACGGGUACGCCAA...</td>\n      <td>.....((((((((((((((((...)))).)))).((((((((((.....</td>\n      <td>2</td>\n      <td>0.981885</td>\n      <td>3</td>\n      <td>EEEEESSSSSSSSSSSSSSSSHHHSSSSBSSSSMSSSSSSSSSSHH...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>id_18ff9d670</td>\n      <td>GGAAAGAGCUCGUGAGAAGAAUCUAGUACAUGCAUACGCUACAUCU...</td>\n      <td>.....(((.((.......((..((((.....((....)).....))...</td>\n      <td>0</td>\n      <td>0.887485</td>\n      <td>5</td>\n      <td>EEEEESSSISSIIIIIIISSIISSSSIIIIISSHHHHSSIIIIISS...</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>id_177cd630b</td>\n      <td>GGAAAGAAGUAGCACGGUCCUAAGGUUACUGUAGCUAUGUCCAGCG...</td>\n      <td>(....((.((((((((((((...))..))))).))))).))(((.(...</td>\n      <td>2</td>\n      <td>0.923722</td>\n      <td>3</td>\n      <td>SMMMMSSISSSSSSSSSSSSHHHSSBBSSSSSBSSSSSISSSSSIS...</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>id_17a9ad5b7</td>\n      <td>GGAAAACACUGCAAAAGUCAACGAAGAAGUUGACUAAGAAGUGAUC...</td>\n      <td>......((((((...(((((((......))))))).....((((((...</td>\n      <td>2</td>\n      <td>0.977602</td>\n      <td>3</td>\n      <td>EEEEEESSSSSSMMMSSSSSSSHHHHHHSSSSSSSMMMMMSSSSSS...</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>id_17ab91518</td>\n      <td>GGAAAACGCGCGCGCGCGCGCGCGCGCGCGCGCGCGCGCGCGCGCG...</td>\n      <td>......(((((((((((((((((((((((((((((....)))))))...</td>\n      <td>2</td>\n      <td>0.982851</td>\n      <td>3</td>\n      <td>EEEEEESSSSSSSSSSSSSSSSSSSSSSSSSSSSSHHHHSSSSSSS...</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"papermill":{"duration":0.021646,"end_time":"2020-09-21T09:17:15.064557","exception":false,"start_time":"2020-09-21T09:17:15.042911","status":"completed"},"tags":[]},"cell_type":"markdown","source":"This file was created using ARNIE, ViennaRNA and bpRNA in the following way.\n\nGet candidate structures with different gamma values. \nSee last cell of [How to Use ARNIE on Kaggle Notebook](https://www.kaggle.com/its7171/how-to-use-arnie-on-kaggle-notebook).\n\nRemove the same as the original structure.\n\nGet a structure with the largest score for each sequence.\n\nGet the predicted_loop_type from the sequence and structure.\nSee [How to Generate predicted_loop_type](https://www.kaggle.com/its7171/how-to-generate-predicted-loop-type)."},{"metadata":{"execution":{"iopub.execute_input":"2020-09-21T09:17:15.118744Z","iopub.status.busy":"2020-09-21T09:17:15.117995Z","iopub.status.idle":"2020-09-21T09:17:15.189668Z","shell.execute_reply":"2020-09-21T09:17:15.189122Z"},"papermill":{"duration":0.098946,"end_time":"2020-09-21T09:17:15.189781","exception":false,"start_time":"2020-09-21T09:17:15.090835","status":"completed"},"tags":[],"trusted":true},"cell_type":"code","source":"def aug_data(df):\n    target_df = df.copy()\n    new_df = aug_df[aug_df['id'].isin(target_df['id'])]\n                         \n    del target_df['structure']\n    del target_df['predicted_loop_type']\n    new_df = new_df.merge(target_df, on=['id','sequence'], how='left')\n\n    df['cnt'] = df['id'].map(new_df[['id','cnt']].set_index('id').to_dict()['cnt'])\n    df['log_gamma'] = 100\n    df['score'] = 1.0\n    df = df.append(new_df[df.columns])\n    return df\ntrain = aug_data(train)\ntest = aug_data(test)","execution_count":19,"outputs":[]},{"metadata":{"execution":{"iopub.execute_input":"2020-09-21T09:17:15.230204Z","iopub.status.busy":"2020-09-21T09:17:15.229625Z","iopub.status.idle":"2020-09-21T09:17:15.233748Z","shell.execute_reply":"2020-09-21T09:17:15.23311Z"},"papermill":{"duration":0.025949,"end_time":"2020-09-21T09:17:15.233875","exception":false,"start_time":"2020-09-21T09:17:15.207926","status":"completed"},"tags":[],"trusted":true},"cell_type":"code","source":"if debug:\n    train = train[:200]\n    test = test[:200]","execution_count":20,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{"papermill":{"duration":0.017944,"end_time":"2020-09-21T09:17:15.270492","exception":false,"start_time":"2020-09-21T09:17:15.252548","status":"completed"},"tags":[]},"cell_type":"markdown","source":"## Build and train model"},{"metadata":{"execution":{"iopub.execute_input":"2020-09-21T09:17:15.31024Z","iopub.status.busy":"2020-09-21T09:17:15.309666Z","iopub.status.idle":"2020-09-21T09:17:18.4114Z","shell.execute_reply":"2020-09-21T09:17:18.410696Z"},"papermill":{"duration":3.123525,"end_time":"2020-09-21T09:17:18.411597","exception":false,"start_time":"2020-09-21T09:17:15.288072","status":"completed"},"tags":[],"trusted":true},"cell_type":"code","source":"model = build_model()\nmodel.summary()","execution_count":21,"outputs":[{"output_type":"stream","text":"Model: \"functional_3\"\n__________________________________________________________________________________________________\nLayer (type)                    Output Shape         Param #     Connected to                     \n==================================================================================================\ninput_2 (InputLayer)            [(None, 107, 6)]     0                                            \n__________________________________________________________________________________________________\ntf_op_layer_strided_slice_3 (Te [(None, 107, 3)]     0           input_2[0][0]                    \n__________________________________________________________________________________________________\nembedding_1 (Embedding)         (None, 107, 3, 100)  1400        tf_op_layer_strided_slice_3[0][0]\n__________________________________________________________________________________________________\ntf_op_layer_Reshape_1 (TensorFl [(None, 107, 300)]   0           embedding_1[0][0]                \n__________________________________________________________________________________________________\ntf_op_layer_strided_slice_4 (Te [(None, 107, 3)]     0           input_2[0][0]                    \n__________________________________________________________________________________________________\nconcatenate_1 (Concatenate)     (None, 107, 303)     0           tf_op_layer_Reshape_1[0][0]      \n                                                                 tf_op_layer_strided_slice_4[0][0]\n__________________________________________________________________________________________________\nbidirectional_3 (Bidirectional) (None, 107, 512)     861696      concatenate_1[0][0]              \n__________________________________________________________________________________________________\nbidirectional_4 (Bidirectional) (None, 107, 512)     1182720     bidirectional_3[0][0]            \n__________________________________________________________________________________________________\nbidirectional_5 (Bidirectional) (None, 107, 512)     1182720     bidirectional_4[0][0]            \n__________________________________________________________________________________________________\ntf_op_layer_strided_slice_5 (Te [(None, 68, 512)]    0           bidirectional_5[0][0]            \n__________________________________________________________________________________________________\ndense_1 (Dense)                 (None, 68, 5)        2565        tf_op_layer_strided_slice_5[0][0]\n==================================================================================================\nTotal params: 3,231,101\nTrainable params: 3,231,101\nNon-trainable params: 0\n__________________________________________________________________________________________________\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"FOLD_N=5\ngkf = GroupKFold(n_splits=FOLD_N)\n\ntype=1\nfor cv, (tr_idx, vl_idx) in enumerate(gkf.split(train,  train['reactivity'], train['cluster_id'])):\n    trn = train.iloc[tr_idx]\n    x_trn = preprocess_inputs(trn)\n    y_trn = np.array(trn[pred_cols].values.tolist()).transpose((0, 2, 1))\n    w_trn = np.log(trn.signal_to_noise+1.1)/2\n\n    val = train.iloc[vl_idx]\n    x_val_all = preprocess_inputs(val)\n    val = val[val.SN_filter == 1]\n    x_val = preprocess_inputs(val)\n    y_val = np.array(val[pred_cols].values.tolist()).transpose((0, 2, 1))\n\n    model = build_model(type=1)\n    model_short = build_model(seq_len=107, pred_len=107,type=type)\n    model_long = build_model(seq_len=130, pred_len=130,type=type)\n\n    history = model.fit(\n        x_trn, y_trn,\n        validation_data = (x_val, y_val),\n        batch_size=64,\n        epochs=100,\n        sample_weight=w_trn,\n        callbacks=[\n            tf.keras.callbacks.ReduceLROnPlateau(),\n            tf.keras.callbacks.ModelCheckpoint(f'model{Ver}_cv{cv}.h5')\n        ]\n        )\n    print(f\"Min training loss={min(history.history['loss'])}, min validation loss={min(history.history['val_loss'])}\")\n    fig = px.line(history.history, y=['loss', 'val_loss'], labels={'index': 'epoch', 'value': 'Mean Cloumnwise Root Mean Squared Error'}, \n    title='Training History')\n    fig.show()\n","execution_count":24,"outputs":[{"output_type":"stream","text":"Epoch 1/100\n60/60 [==============================] - 7s 122ms/step - loss: 0.3317 - val_loss: 0.3577\nEpoch 2/100\n60/60 [==============================] - 6s 92ms/step - loss: 0.2780 - val_loss: 0.3087\nEpoch 3/100\n60/60 [==============================] - 6s 96ms/step - loss: 0.2575 - val_loss: 0.2892\nEpoch 4/100\n60/60 [==============================] - 6s 92ms/step - loss: 0.2428 - val_loss: 0.2712\nEpoch 5/100\n60/60 [==============================] - 6s 95ms/step - loss: 0.2293 - val_loss: 0.2608\nEpoch 6/100\n60/60 [==============================] - 6s 93ms/step - loss: 0.2194 - val_loss: 0.2506\nEpoch 7/100\n60/60 [==============================] - 6s 95ms/step - loss: 0.2109 - val_loss: 0.2444\nEpoch 8/100\n60/60 [==============================] - 6s 92ms/step - loss: 0.2028 - val_loss: 0.2396\nEpoch 9/100\n60/60 [==============================] - 6s 95ms/step - loss: 0.1973 - val_loss: 0.2335\nEpoch 10/100\n60/60 [==============================] - 6s 92ms/step - loss: 0.1923 - val_loss: 0.2323\nEpoch 11/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1886 - val_loss: 0.2305\nEpoch 12/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1838 - val_loss: 0.2288\nEpoch 13/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1799 - val_loss: 0.2324\nEpoch 14/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1765 - val_loss: 0.2281\nEpoch 15/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1727 - val_loss: 0.2274\nEpoch 16/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1698 - val_loss: 0.2262\nEpoch 17/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1668 - val_loss: 0.2253\nEpoch 18/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1635 - val_loss: 0.2291\nEpoch 19/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1615 - val_loss: 0.2265\nEpoch 20/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1588 - val_loss: 0.2279\nEpoch 21/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1564 - val_loss: 0.2281\nEpoch 22/100\n60/60 [==============================] - 6s 92ms/step - loss: 0.1542 - val_loss: 0.2269\nEpoch 23/100\n60/60 [==============================] - 6s 95ms/step - loss: 0.1522 - val_loss: 0.2273\nEpoch 24/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1505 - val_loss: 0.2276\nEpoch 25/100\n60/60 [==============================] - 6s 95ms/step - loss: 0.1488 - val_loss: 0.2277\nEpoch 26/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1473 - val_loss: 0.2278\nEpoch 27/100\n60/60 [==============================] - 6s 95ms/step - loss: 0.1456 - val_loss: 0.2281\nEpoch 28/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1416 - val_loss: 0.2261\nEpoch 29/100\n60/60 [==============================] - 6s 95ms/step - loss: 0.1403 - val_loss: 0.2261\nEpoch 30/100\n60/60 [==============================] - 6s 93ms/step - loss: 0.1396 - val_loss: 0.2263\nEpoch 31/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1392 - val_loss: 0.2260\nEpoch 32/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1387 - val_loss: 0.2260\nEpoch 33/100\n60/60 [==============================] - 6s 95ms/step - loss: 0.1386 - val_loss: 0.2266\nEpoch 34/100\n60/60 [==============================] - 6s 93ms/step - loss: 0.1381 - val_loss: 0.2263\nEpoch 35/100\n60/60 [==============================] - 6s 95ms/step - loss: 0.1380 - val_loss: 0.2264\nEpoch 36/100\n60/60 [==============================] - 6s 95ms/step - loss: 0.1378 - val_loss: 0.2265\nEpoch 37/100\n60/60 [==============================] - 6s 96ms/step - loss: 0.1375 - val_loss: 0.2265\nEpoch 38/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1372 - val_loss: 0.2265\nEpoch 39/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1370 - val_loss: 0.2265\nEpoch 40/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1369 - val_loss: 0.2265\nEpoch 41/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1369 - val_loss: 0.2264\nEpoch 42/100\n60/60 [==============================] - 6s 93ms/step - loss: 0.1368 - val_loss: 0.2265\nEpoch 43/100\n60/60 [==============================] - 6s 93ms/step - loss: 0.1369 - val_loss: 0.2264\nEpoch 44/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1367 - val_loss: 0.2264\nEpoch 45/100\n60/60 [==============================] - 6s 93ms/step - loss: 0.1366 - val_loss: 0.2264\nEpoch 46/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1368 - val_loss: 0.2265\nEpoch 47/100\n60/60 [==============================] - 6s 95ms/step - loss: 0.1367 - val_loss: 0.2264\nEpoch 48/100\n60/60 [==============================] - 6s 98ms/step - loss: 0.1365 - val_loss: 0.2265\nEpoch 49/100\n60/60 [==============================] - 6s 95ms/step - loss: 0.1366 - val_loss: 0.2265\nEpoch 50/100\n60/60 [==============================] - 6s 93ms/step - loss: 0.1364 - val_loss: 0.2265\nEpoch 51/100\n60/60 [==============================] - 6s 93ms/step - loss: 0.1366 - val_loss: 0.2265\nEpoch 52/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1365 - val_loss: 0.2265\nEpoch 53/100\n60/60 [==============================] - 6s 95ms/step - loss: 0.1367 - val_loss: 0.2265\nEpoch 54/100\n60/60 [==============================] - 6s 93ms/step - loss: 0.1366 - val_loss: 0.2265\nEpoch 55/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1366 - val_loss: 0.2265\nEpoch 56/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1365 - val_loss: 0.2265\nEpoch 57/100\n60/60 [==============================] - 6s 95ms/step - loss: 0.1366 - val_loss: 0.2265\nEpoch 58/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1366 - val_loss: 0.2265\nEpoch 59/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1365 - val_loss: 0.2265\nEpoch 60/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1368 - val_loss: 0.2265\nEpoch 61/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1367 - val_loss: 0.2265\nEpoch 62/100\n60/60 [==============================] - 6s 93ms/step - loss: 0.1365 - val_loss: 0.2265\nEpoch 63/100\n60/60 [==============================] - 6s 95ms/step - loss: 0.1365 - val_loss: 0.2265\nEpoch 64/100\n60/60 [==============================] - 6s 93ms/step - loss: 0.1366 - val_loss: 0.2265\nEpoch 65/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1365 - val_loss: 0.2265\nEpoch 66/100\n60/60 [==============================] - 6s 93ms/step - loss: 0.1365 - val_loss: 0.2265\nEpoch 67/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1365 - val_loss: 0.2265\nEpoch 68/100\n60/60 [==============================] - 6s 95ms/step - loss: 0.1366 - val_loss: 0.2265\nEpoch 69/100\n60/60 [==============================] - 6s 95ms/step - loss: 0.1366 - val_loss: 0.2265\nEpoch 70/100\n60/60 [==============================] - 6s 93ms/step - loss: 0.1367 - val_loss: 0.2265\nEpoch 71/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1366 - val_loss: 0.2265\nEpoch 72/100\n60/60 [==============================] - 6s 93ms/step - loss: 0.1367 - val_loss: 0.2265\nEpoch 73/100\n60/60 [==============================] - 6s 93ms/step - loss: 0.1366 - val_loss: 0.2265\nEpoch 74/100\n60/60 [==============================] - 6s 93ms/step - loss: 0.1366 - val_loss: 0.2265\nEpoch 75/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1366 - val_loss: 0.2265\nEpoch 76/100\n60/60 [==============================] - 6s 93ms/step - loss: 0.1365 - val_loss: 0.2265\nEpoch 77/100\n60/60 [==============================] - 6s 95ms/step - loss: 0.1367 - val_loss: 0.2265\nEpoch 78/100\n60/60 [==============================] - 6s 95ms/step - loss: 0.1365 - val_loss: 0.2265\nEpoch 79/100\n60/60 [==============================] - 6s 95ms/step - loss: 0.1366 - val_loss: 0.2265\nEpoch 80/100\n60/60 [==============================] - 6s 93ms/step - loss: 0.1366 - val_loss: 0.2265\nEpoch 81/100\n60/60 [==============================] - 6s 95ms/step - loss: 0.1365 - val_loss: 0.2265\nEpoch 82/100\n","name":"stdout"},{"output_type":"stream","text":"60/60 [==============================] - 6s 94ms/step - loss: 0.1366 - val_loss: 0.2265\nEpoch 83/100\n60/60 [==============================] - 6s 93ms/step - loss: 0.1366 - val_loss: 0.2265\nEpoch 84/100\n60/60 [==============================] - 6s 93ms/step - loss: 0.1366 - val_loss: 0.2265\nEpoch 85/100\n60/60 [==============================] - 6s 95ms/step - loss: 0.1366 - val_loss: 0.2265\nEpoch 86/100\n60/60 [==============================] - 6s 93ms/step - loss: 0.1366 - val_loss: 0.2265\nEpoch 87/100\n60/60 [==============================] - 6s 95ms/step - loss: 0.1367 - val_loss: 0.2265\nEpoch 88/100\n60/60 [==============================] - 6s 92ms/step - loss: 0.1367 - val_loss: 0.2265\nEpoch 89/100\n60/60 [==============================] - 6s 97ms/step - loss: 0.1367 - val_loss: 0.2265\nEpoch 90/100\n60/60 [==============================] - 6s 93ms/step - loss: 0.1365 - val_loss: 0.2265\nEpoch 91/100\n60/60 [==============================] - 6s 95ms/step - loss: 0.1364 - val_loss: 0.2265\nEpoch 92/100\n60/60 [==============================] - 6s 93ms/step - loss: 0.1365 - val_loss: 0.2265\nEpoch 93/100\n60/60 [==============================] - 6s 95ms/step - loss: 0.1368 - val_loss: 0.2265\nEpoch 94/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1366 - val_loss: 0.2265\nEpoch 95/100\n60/60 [==============================] - 6s 95ms/step - loss: 0.1365 - val_loss: 0.2265\nEpoch 96/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1364 - val_loss: 0.2265\nEpoch 97/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1366 - val_loss: 0.2265\nEpoch 98/100\n60/60 [==============================] - 6s 92ms/step - loss: 0.1366 - val_loss: 0.2265\nEpoch 99/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1367 - val_loss: 0.2265\nEpoch 100/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1365 - val_loss: 0.2265\nMin training loss=0.1363956332206726, min validation loss=0.22532330453395844\n","name":"stdout"},{"output_type":"display_data","data":{"text/html":"        <script type=\"text/javascript\">\n        window.PlotlyConfig = {MathJaxConfig: 'local'};\n        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n        if (typeof require !== 'undefined') {\n        require.undef(\"plotly\");\n        requirejs.config({\n            paths: {\n                'plotly': 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93ms/step - loss: 0.1791 - val_loss: 0.2259\nEpoch 13/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1748 - val_loss: 0.2276\nEpoch 14/100\n60/60 [==============================] - 6s 92ms/step - loss: 0.1719 - val_loss: 0.2256\nEpoch 15/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1687 - val_loss: 0.2270\nEpoch 16/100\n60/60 [==============================] - 6s 93ms/step - loss: 0.1654 - val_loss: 0.2278\nEpoch 17/100\n60/60 [==============================] - 6s 95ms/step - loss: 0.1630 - val_loss: 0.2264\nEpoch 18/100\n60/60 [==============================] - 6s 92ms/step - loss: 0.1600 - val_loss: 0.2263\nEpoch 19/100\n60/60 [==============================] - 6s 95ms/step - loss: 0.1576 - val_loss: 0.2232\nEpoch 20/100\n60/60 [==============================] - 6s 93ms/step - loss: 0.1552 - val_loss: 0.2239\nEpoch 21/100\n60/60 [==============================] - 6s 95ms/step - loss: 0.1529 - val_loss: 0.2243\nEpoch 22/100\n60/60 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{\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Training History\"}, \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"epoch\"}}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"Mean Squared Error\"}}},\n                        {\"responsive\": true}\n                    ).then(function(){\n                            \nvar gd = document.getElementById('83cf81b0-195e-43eb-b654-22e2564019f3');\nvar x = new MutationObserver(function (mutations, observer) {{\n        var display = window.getComputedStyle(gd).display;\n        if (!display || display === 'none') {{\n            console.log([gd, 'removed!']);\n            Plotly.purge(gd);\n            observer.disconnect();\n        }}\n}});\n\n// Listen for the removal of the full notebook cells\nvar notebookContainer = gd.closest('#notebook-container');\nif (notebookContainer) {{\n    x.observe(notebookContainer, {childList: 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build_model(seq_len=107, pred_len=107,type=type)\n    model_long = build_model(seq_len=130, pred_len=130,type=type)\n\n    history = model.fit(\n        x_trn, y_trn,\n        validation_data = (x_val, y_val),\n        batch_size=64,\n        epochs=100,\n        sample_weight=w_trn,\n        callbacks=[\n            tf.keras.callbacks.ReduceLROnPlateau(),\n            tf.keras.callbacks.ModelCheckpoint(f'model{Ver}_cv{cv}.h5')\n        ]\n        )\n    print(f\"Min training loss={min(history.history['loss'])}, min validation loss={min(history.history['val_loss'])}\")\n    fig = px.line(history.history, y=['loss', 'val_loss'], labels={'index': 'epoch', 'value': 'Mean Cloumnwise Root Mean Squared Error'}, \n    title='Training History')\n    fig.show()\n","execution_count":25,"outputs":[{"output_type":"stream","text":"Epoch 1/100\n60/60 [==============================] - 7s 122ms/step - loss: 0.3426 - val_loss: 0.3616\nEpoch 2/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.2839 - val_loss: 0.3131\nEpoch 3/100\n60/60 [==============================] - 6s 93ms/step - loss: 0.2593 - val_loss: 0.2995\nEpoch 4/100\n60/60 [==============================] - 6s 93ms/step - loss: 0.2465 - val_loss: 0.2750\nEpoch 5/100\n60/60 [==============================] - 6s 93ms/step - loss: 0.2322 - val_loss: 0.2608\nEpoch 6/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.2218 - val_loss: 0.2531\nEpoch 7/100\n60/60 [==============================] - 6s 93ms/step - loss: 0.2134 - val_loss: 0.2443\nEpoch 8/100\n60/60 [==============================] - 6s 93ms/step - loss: 0.2048 - val_loss: 0.2388\nEpoch 9/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1994 - val_loss: 0.2366\nEpoch 10/100\n60/60 [==============================] - 6s 95ms/step - loss: 0.1950 - val_loss: 0.2336\nEpoch 11/100\n60/60 [==============================] - 6s 93ms/step - loss: 0.1900 - val_loss: 0.2330\nEpoch 12/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1861 - val_loss: 0.2324\nEpoch 13/100\n60/60 [==============================] - 6s 93ms/step - loss: 0.1815 - val_loss: 0.2326\nEpoch 14/100\n60/60 [==============================] - 6s 93ms/step - loss: 0.1780 - val_loss: 0.2327\nEpoch 15/100\n60/60 [==============================] - 6s 93ms/step - loss: 0.1742 - val_loss: 0.2297\nEpoch 16/100\n60/60 [==============================] - 6s 95ms/step - loss: 0.1706 - val_loss: 0.2296\nEpoch 17/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1679 - val_loss: 0.2296\nEpoch 18/100\n60/60 [==============================] - 6s 93ms/step - loss: 0.1652 - val_loss: 0.2310\nEpoch 19/100\n60/60 [==============================] - 6s 93ms/step - loss: 0.1623 - val_loss: 0.2291\nEpoch 20/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1595 - val_loss: 0.2297\nEpoch 21/100\n60/60 [==============================] - 6s 93ms/step - loss: 0.1576 - val_loss: 0.2315\nEpoch 22/100\n60/60 [==============================] - 6s 93ms/step - loss: 0.1556 - val_loss: 0.2305\nEpoch 23/100\n60/60 [==============================] - 6s 95ms/step - loss: 0.1530 - val_loss: 0.2285\nEpoch 24/100\n60/60 [==============================] - 6s 93ms/step - loss: 0.1515 - val_loss: 0.2295\nEpoch 25/100\n60/60 [==============================] - 6s 93ms/step - loss: 0.1490 - val_loss: 0.2311\nEpoch 26/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1477 - val_loss: 0.2306\nEpoch 27/100\n60/60 [==============================] - 6s 93ms/step - loss: 0.1460 - val_loss: 0.2306\nEpoch 28/100\n60/60 [==============================] - 6s 93ms/step - loss: 0.1444 - val_loss: 0.2305\nEpoch 29/100\n60/60 [==============================] - 6s 95ms/step - loss: 0.1426 - val_loss: 0.2293\nEpoch 30/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1416 - val_loss: 0.2306\nEpoch 31/100\n60/60 [==============================] - 6s 93ms/step - loss: 0.1402 - val_loss: 0.2297\nEpoch 32/100\n60/60 [==============================] - 6s 95ms/step - loss: 0.1389 - val_loss: 0.2314\nEpoch 33/100\n60/60 [==============================] - 6s 93ms/step - loss: 0.1377 - val_loss: 0.2343\nEpoch 34/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1343 - val_loss: 0.2312\nEpoch 35/100\n60/60 [==============================] - 6s 95ms/step - loss: 0.1328 - val_loss: 0.2304\nEpoch 36/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1321 - val_loss: 0.2303\nEpoch 37/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1319 - val_loss: 0.2304\nEpoch 38/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1315 - val_loss: 0.2306\nEpoch 39/100\n60/60 [==============================] - 6s 98ms/step - loss: 0.1312 - val_loss: 0.2309\nEpoch 40/100\n60/60 [==============================] - 6s 95ms/step - loss: 0.1310 - val_loss: 0.2304\nEpoch 41/100\n60/60 [==============================] - 6s 93ms/step - loss: 0.1309 - val_loss: 0.2306\nEpoch 42/100\n60/60 [==============================] - 6s 95ms/step - loss: 0.1305 - val_loss: 0.2305\nEpoch 43/100\n60/60 [==============================] - 6s 93ms/step - loss: 0.1303 - val_loss: 0.2306\nEpoch 44/100\n60/60 [==============================] - 6s 95ms/step - loss: 0.1299 - val_loss: 0.2306\nEpoch 45/100\n60/60 [==============================] - 6s 92ms/step - loss: 0.1299 - val_loss: 0.2304\nEpoch 46/100\n60/60 [==============================] - 6s 95ms/step - loss: 0.1298 - val_loss: 0.2306\nEpoch 47/100\n60/60 [==============================] - 6s 93ms/step - loss: 0.1299 - val_loss: 0.2306\nEpoch 48/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1297 - val_loss: 0.2306\nEpoch 49/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1297 - val_loss: 0.2305\nEpoch 50/100\n60/60 [==============================] - 6s 96ms/step - loss: 0.1297 - val_loss: 0.2307\nEpoch 51/100\n60/60 [==============================] - 6s 93ms/step - loss: 0.1296 - val_loss: 0.2307\nEpoch 52/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1296 - val_loss: 0.2308\nEpoch 53/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1297 - val_loss: 0.2307\nEpoch 54/100\n60/60 [==============================] - 6s 93ms/step - loss: 0.1295 - val_loss: 0.2307\nEpoch 55/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1295 - val_loss: 0.2307\nEpoch 56/100\n60/60 [==============================] - 6s 93ms/step - loss: 0.1295 - val_loss: 0.2307\nEpoch 57/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1297 - val_loss: 0.2308\nEpoch 58/100\n60/60 [==============================] - 6s 95ms/step - loss: 0.1296 - val_loss: 0.2307\nEpoch 59/100\n60/60 [==============================] - 6s 93ms/step - loss: 0.1296 - val_loss: 0.2307\nEpoch 60/100\n60/60 [==============================] - 6s 96ms/step - loss: 0.1296 - val_loss: 0.2308\nEpoch 61/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1296 - val_loss: 0.2307\nEpoch 62/100\n60/60 [==============================] - 6s 95ms/step - loss: 0.1295 - val_loss: 0.2307\nEpoch 63/100\n60/60 [==============================] - 6s 92ms/step - loss: 0.1295 - val_loss: 0.2307\nEpoch 64/100\n60/60 [==============================] - 6s 95ms/step - loss: 0.1296 - val_loss: 0.2307\nEpoch 65/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1295 - val_loss: 0.2307\nEpoch 66/100\n60/60 [==============================] - 6s 93ms/step - loss: 0.1296 - val_loss: 0.2307\nEpoch 67/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1295 - val_loss: 0.2307\nEpoch 68/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1296 - val_loss: 0.2307\nEpoch 69/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1294 - val_loss: 0.2307\nEpoch 70/100\n60/60 [==============================] - 6s 97ms/step - loss: 0.1295 - val_loss: 0.2307\nEpoch 71/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1297 - val_loss: 0.2307\nEpoch 72/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1295 - val_loss: 0.2307\nEpoch 73/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1296 - val_loss: 0.2307\nEpoch 74/100\n60/60 [==============================] - 6s 93ms/step - loss: 0.1296 - val_loss: 0.2307\nEpoch 75/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1297 - val_loss: 0.2307\nEpoch 76/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1295 - val_loss: 0.2307\nEpoch 77/100\n60/60 [==============================] - 6s 93ms/step - loss: 0.1293 - val_loss: 0.2307\nEpoch 78/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1296 - val_loss: 0.2307\nEpoch 79/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1295 - val_loss: 0.2307\nEpoch 80/100\n60/60 [==============================] - 6s 96ms/step - loss: 0.1295 - val_loss: 0.2307\nEpoch 81/100\n60/60 [==============================] - 6s 95ms/step - loss: 0.1295 - val_loss: 0.2307\nEpoch 82/100\n","name":"stdout"},{"output_type":"stream","text":"60/60 [==============================] - 6s 94ms/step - loss: 0.1295 - val_loss: 0.2307\nEpoch 83/100\n60/60 [==============================] - 6s 95ms/step - loss: 0.1296 - val_loss: 0.2307\nEpoch 84/100\n60/60 [==============================] - 6s 95ms/step - loss: 0.1297 - val_loss: 0.2307\nEpoch 85/100\n60/60 [==============================] - 6s 95ms/step - loss: 0.1296 - val_loss: 0.2307\nEpoch 86/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1296 - val_loss: 0.2307\nEpoch 87/100\n60/60 [==============================] - 6s 93ms/step - loss: 0.1296 - val_loss: 0.2307\nEpoch 88/100\n60/60 [==============================] - 6s 95ms/step - loss: 0.1296 - val_loss: 0.2307\nEpoch 89/100\n60/60 [==============================] - 6s 93ms/step - loss: 0.1294 - val_loss: 0.2307\nEpoch 90/100\n60/60 [==============================] - 6s 95ms/step - loss: 0.1295 - val_loss: 0.2307\nEpoch 91/100\n60/60 [==============================] - 6s 95ms/step - loss: 0.1295 - val_loss: 0.2307\nEpoch 92/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1295 - val_loss: 0.2307\nEpoch 93/100\n60/60 [==============================] - 6s 93ms/step - loss: 0.1296 - val_loss: 0.2307\nEpoch 94/100\n60/60 [==============================] - 6s 95ms/step - loss: 0.1295 - val_loss: 0.2307\nEpoch 95/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1295 - val_loss: 0.2307\nEpoch 96/100\n60/60 [==============================] - 6s 93ms/step - loss: 0.1296 - val_loss: 0.2307\nEpoch 97/100\n60/60 [==============================] - 6s 93ms/step - loss: 0.1296 - val_loss: 0.2307\nEpoch 98/100\n60/60 [==============================] - 6s 95ms/step - loss: 0.1296 - val_loss: 0.2307\nEpoch 99/100\n60/60 [==============================] - 6s 93ms/step - loss: 0.1295 - val_loss: 0.2307\nEpoch 100/100\n60/60 [==============================] - 6s 93ms/step - loss: 0.1295 - val_loss: 0.2307\nMin training loss=0.12931862473487854, min validation loss=0.22848781943321228\n","name":"stdout"},{"output_type":"display_data","data":{"text/html":"<div>\n        \n        \n            <div id=\"d0fa53b8-5660-4682-878e-6413c13c49e1\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>\n            <script type=\"text/javascript\">\n                require([\"plotly\"], function(Plotly) {\n                    window.PLOTLYENV=window.PLOTLYENV || {};\n                    \n                if (document.getElementById(\"d0fa53b8-5660-4682-878e-6413c13c49e1\")) {\n                    Plotly.newPlot(\n                        'd0fa53b8-5660-4682-878e-6413c13c49e1',\n                        [{\"hovertemplate\": 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[==============================] - 6s 94ms/step - loss: 0.2178 - val_loss: 0.2546\nEpoch 7/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.2071 - val_loss: 0.2473\nEpoch 8/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1989 - val_loss: 0.2440\nEpoch 9/100\n60/60 [==============================] - 6s 93ms/step - loss: 0.1936 - val_loss: 0.2407\nEpoch 10/100\n60/60 [==============================] - 5s 92ms/step - loss: 0.1889 - val_loss: 0.2393\nEpoch 11/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1844 - val_loss: 0.2388\nEpoch 12/100\n60/60 [==============================] - 6s 93ms/step - loss: 0.1804 - val_loss: 0.2357\nEpoch 13/100\n60/60 [==============================] - 6s 92ms/step - loss: 0.1767 - val_loss: 0.2382\nEpoch 14/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1731 - val_loss: 0.2368\nEpoch 15/100\n60/60 [==============================] - 6s 93ms/step - loss: 0.1701 - val_loss: 0.2369\nEpoch 16/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1666 - val_loss: 0.2348\nEpoch 17/100\n60/60 [==============================] - 6s 92ms/step - loss: 0.1638 - val_loss: 0.2334\nEpoch 18/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1609 - val_loss: 0.2331\nEpoch 19/100\n60/60 [==============================] - 6s 93ms/step - loss: 0.1584 - val_loss: 0.2318\nEpoch 20/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1563 - val_loss: 0.2332\nEpoch 21/100\n60/60 [==============================] - 5s 91ms/step - loss: 0.1541 - val_loss: 0.2327\nEpoch 22/100\n60/60 [==============================] - 6s 95ms/step - loss: 0.1521 - val_loss: 0.2329\nEpoch 23/100\n60/60 [==============================] - 6s 92ms/step - loss: 0.1502 - val_loss: 0.2325\nEpoch 24/100\n60/60 [==============================] - 6s 93ms/step - loss: 0.1482 - val_loss: 0.2342\nEpoch 25/100\n60/60 [==============================] - 6s 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6s 96ms/step - loss: 0.1362 - val_loss: 0.2316\nEpoch 33/100\n60/60 [==============================] - 6s 95ms/step - loss: 0.1329 - val_loss: 0.2291\nEpoch 34/100\n60/60 [==============================] - 6s 95ms/step - loss: 0.1314 - val_loss: 0.2293\nEpoch 35/100\n60/60 [==============================] - 6s 95ms/step - loss: 0.1308 - val_loss: 0.2294\nEpoch 36/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1305 - val_loss: 0.2291\nEpoch 37/100\n60/60 [==============================] - 6s 94ms/step - loss: 0.1302 - val_loss: 0.2296\nEpoch 38/100\n60/60 [==============================] - 6s 95ms/step - loss: 0.1300 - val_loss: 0.2295\nEpoch 39/100\n60/60 [==============================] - 6s 93ms/step - loss: 0.1298 - val_loss: 0.2296\nEpoch 40/100\n60/60 [==============================] - 6s 95ms/step - loss: 0.1295 - val_loss: 0.2291\nEpoch 41/100\n60/60 [==============================] - 6s 93ms/step - loss: 0.1292 - val_loss: 0.2294\nEpoch 42/100\n60/60 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GroupKFold(n_splits=FOLD_N)\n\ntype=2\nfor cv, (tr_idx, vl_idx) in enumerate(gkf.split(train,  train['reactivity'], train['cluster_id'])):\n    trn = train.iloc[tr_idx]\n    x_trn = preprocess_inputs(trn)\n    y_trn = np.array(trn[pred_cols].values.tolist()).transpose((0, 2, 1))\n    w_trn = np.log(trn.signal_to_noise+1.1)/2\n\n    val = train.iloc[vl_idx]\n    x_val_all = preprocess_inputs(val)\n    val = val[val.SN_filter == 1]\n    x_val = preprocess_inputs(val)\n    y_val = np.array(val[pred_cols].values.tolist()).transpose((0, 2, 1))\n\n    model = build_model(type=1)\n    model_short = build_model(seq_len=107, pred_len=107,type=type)\n    model_long = build_model(seq_len=130, pred_len=130,type=type)\n\n    history = model.fit(\n        x_trn, y_trn,\n        validation_data = (x_val, y_val),\n        batch_size=64,\n        epochs=100,\n        sample_weight=w_trn,\n        callbacks=[\n            tf.keras.callbacks.ReduceLROnPlateau(),\n            tf.keras.callbacks.ModelCheckpoint(f'model{Ver}_cv{cv}.h5')\n        ]\n        )\n    print(f\"Min training loss={min(history.history['loss'])}, min validation loss={min(history.history['val_loss'])}\")\n    fig = px.line(history.history, y=['loss', 'val_loss'], labels={'index': 'epoch', 'value': 'Mean Cloumnwise Root Mean Squared Error'}, \n    title='Training History')\n    fig.show()\n","execution_count":null,"outputs":[]},{"metadata":{"execution":{"iopub.execute_input":"2020-09-21T09:17:18.473586Z","iopub.status.busy":"2020-09-21T09:17:18.472319Z","iopub.status.idle":"2020-09-21T09:17:18.475796Z","shell.execute_reply":"2020-09-21T09:17:18.47518Z"},"incorrectly_encoded_metadata":"_kg_hide-output=true","papermill":{"duration":0.043776,"end_time":"2020-09-21T09:17:18.475902","exception":false,"start_time":"2020-09-21T09:17:18.432126","status":"completed"},"scrolled":true,"tags":[],"trusted":true},"cell_type":"code","source":"def train_and_predict(type = 0, FOLD_N = 5):\n    \n    gkf = GroupKFold(n_splits=FOLD_N)\n\n    public_df = test.query(\"seq_length == 107\").copy()\n    private_df = test.query(\"seq_length == 130\").copy()\n\n    public_inputs = preprocess_inputs(public_df)\n    private_inputs = preprocess_inputs(private_df)\n\n\n    holdouts = []\n    holdout_preds = []\n\n    for cv, (tr_idx, vl_idx) in enumerate(gkf.split(train,  train['reactivity'], train['cluster_id'])):\n        trn = train.iloc[tr_idx]\n        x_trn = preprocess_inputs(trn)\n        y_trn = np.array(trn[pred_cols].values.tolist()).transpose((0, 2, 1))\n        w_trn = np.log(trn.signal_to_noise+1.1)/2\n\n        val = train.iloc[vl_idx]\n        x_val_all = preprocess_inputs(val)\n        val = val[val.SN_filter == 1]\n        x_val = preprocess_inputs(val)\n        y_val = np.array(val[pred_cols].values.tolist()).transpose((0, 2, 1))\n\n        model = build_model(type=type)\n        model_short = build_model(seq_len=107, pred_len=107,type=type)\n        model_long = build_model(seq_len=130, pred_len=130,type=type)\n\n        history = model.fit(\n            x_trn, y_trn,\n            validation_data = (x_val, y_val),\n            batch_size=64,\n            epochs=100,\n            sample_weight=w_trn,\n            callbacks=[\n                tf.keras.callbacks.ReduceLROnPlateau(),\n                tf.keras.callbacks.ModelCheckpoint(f'model{Ver}_cv{cv}.h5')\n            ]\n        )\n\n        fig = px.line(\n            history.history, y=['loss', 'val_loss'], \n            labels={'index': 'epoch', 'value': 'Mean Squared Error'}, \n            title='Training History')\n        fig.show()\n\n        model.load_weights(f'model{Ver}_cv{cv}.h5')\n        model_short.load_weights(f'model{Ver}_cv{cv}.h5')\n        model_long.load_weights(f'model{Ver}_cv{cv}.h5')\n\n        holdouts.append(train.iloc[vl_idx])\n        holdout_preds.append(model.predict(x_val_all))\n        if cv == 0:\n            public_preds = model_short.predict(public_inputs)/FOLD_N\n            private_preds = model_long.predict(private_inputs)/FOLD_N\n        else:\n            public_preds += model_short.predict(public_inputs)/FOLD_N\n            private_preds += model_long.predict(private_inputs)/FOLD_N\n    return holdouts, holdout_preds, public_df, public_preds, private_df, private_preds","execution_count":11,"outputs":[]},{"metadata":{"_kg_hide-output":true,"execution":{"iopub.execute_input":"2020-09-21T09:17:18.526941Z","iopub.status.busy":"2020-09-21T09:17:18.525944Z","iopub.status.idle":"2020-09-21T09:55:34.97759Z","shell.execute_reply":"2020-09-21T09:55:34.873937Z"},"papermill":{"duration":2296.482954,"end_time":"2020-09-21T09:55:34.97778","exception":false,"start_time":"2020-09-21T09:17:18.494826","status":"completed"},"tags":[],"trusted":true},"cell_type":"code","source":"val_df, val_preds, test_df, test_preds = [], [], [], []\nif debug:\n    nmodel = 1\nelse:\n    nmodel = 3\nfor i in range(nmodel):\n    holdouts, holdout_preds, public_df, public_preds, private_df, private_preds = train_and_predict(i)\n    val_df += holdouts\n    val_preds += holdout_preds\n    test_df.append(public_df)\n    test_df.append(private_df)\n    test_preds.append(public_preds)\n    test_preds.append(private_preds)","execution_count":12,"outputs":[{"output_type":"stream","text":"Epoch 1/100\n60/60 [==============================] - 7s 119ms/step - loss: 0.3363 - val_loss: 0.3621\nEpoch 2/100\n60/60 [==============================] - 5s 89ms/step - loss: 0.2840 - val_loss: 0.3168\nEpoch 3/100\n60/60 [==============================] - 5s 88ms/step - loss: 0.2649 - val_loss: 0.3072\nEpoch 4/100\n60/60 [==============================] - 5s 88ms/step - loss: 0.2530 - val_loss: 0.2842\nEpoch 5/100\n60/60 [==============================] - 5s 89ms/step - loss: 0.2422 - val_loss: 0.2718\nEpoch 6/100\n60/60 [==============================] - 5s 88ms/step - loss: 0.2316 - val_loss: 0.2586\nEpoch 7/100\n60/60 [==============================] - 5s 89ms/step - loss: 0.2238 - val_loss: 0.2562\nEpoch 8/100\n60/60 [==============================] - 5s 88ms/step - loss: 0.2161 - val_loss: 0.2476\nEpoch 9/100\n60/60 [==============================] - 5s 88ms/step - loss: 0.2094 - val_loss: 0.2429\nEpoch 10/100\n60/60 [==============================] - 5s 89ms/step - loss: 0.2037 - val_loss: 0.2375\nEpoch 11/100\n60/60 [==============================] - 5s 91ms/step - loss: 0.2005 - val_loss: 0.2384\nEpoch 12/100\n60/60 [==============================] - 5s 89ms/step - loss: 0.1959 - val_loss: 0.2341\nEpoch 13/100\n60/60 [==============================] - 5s 88ms/step - loss: 0.1934 - val_loss: 0.2336\nEpoch 14/100\n60/60 [==============================] - 5s 88ms/step - loss: 0.1902 - val_loss: 0.2305\nEpoch 15/100\n60/60 [==============================] - 5s 88ms/step - loss: 0.1861 - val_loss: 0.2293\nEpoch 16/100\n60/60 [==============================] - 5s 89ms/step - loss: 0.1838 - val_loss: 0.2284\nEpoch 17/100\n60/60 [==============================] - 5s 88ms/step - loss: 0.1807 - val_loss: 0.2292\nEpoch 18/100\n60/60 [==============================] - 5s 88ms/step - loss: 0.1784 - val_loss: 0.2292\nEpoch 19/100\n60/60 [==============================] - 5s 89ms/step - loss: 0.1760 - val_loss: 0.2291\nEpoch 20/100\n60/60 [==============================] - 5s 89ms/step - loss: 0.1735 - val_loss: 0.2287\nEpoch 21/100\n60/60 [==============================] - 5s 89ms/step - loss: 0.1714 - val_loss: 0.2270\nEpoch 22/100\n60/60 [==============================] - 5s 91ms/step - loss: 0.1690 - val_loss: 0.2261\nEpoch 23/100\n60/60 [==============================] - 5s 89ms/step - loss: 0.1677 - val_loss: 0.2273\nEpoch 24/100\n60/60 [==============================] - 5s 89ms/step - loss: 0.1655 - val_loss: 0.2266\nEpoch 25/100\n60/60 [==============================] - 5s 88ms/step - loss: 0.1639 - val_loss: 0.2273\nEpoch 26/100\n60/60 [==============================] - 5s 88ms/step - loss: 0.1618 - val_loss: 0.2243\nEpoch 27/100\n60/60 [==============================] - 5s 88ms/step - loss: 0.1600 - val_loss: 0.2275\nEpoch 28/100\n60/60 [==============================] - 5s 89ms/step - loss: 0.1594 - val_loss: 0.2302\nEpoch 29/100\n60/60 [==============================] - 5s 88ms/step - loss: 0.1569 - val_loss: 0.2255\nEpoch 30/100\n60/60 [==============================] - 5s 88ms/step - loss: 0.1554 - val_loss: 0.2248\nEpoch 31/100\n60/60 [==============================] - 5s 88ms/step - loss: 0.1540 - val_loss: 0.2265\nEpoch 32/100\n60/60 [==============================] - 5s 88ms/step - loss: 0.1526 - val_loss: 0.2259\nEpoch 33/100\n60/60 [==============================] - 5s 89ms/step - loss: 0.1512 - val_loss: 0.2260\nEpoch 34/100\n60/60 [==============================] - 5s 89ms/step - loss: 0.1504 - val_loss: 0.2273\nEpoch 35/100\n60/60 [==============================] - 5s 89ms/step - loss: 0.1490 - val_loss: 0.2266\nEpoch 36/100\n60/60 [==============================] - 5s 88ms/step - loss: 0.1477 - val_loss: 0.2269\nEpoch 37/100\n60/60 [==============================] - 5s 89ms/step - loss: 0.1435 - val_loss: 0.2238\nEpoch 38/100\n60/60 [==============================] - 5s 88ms/step - loss: 0.1421 - val_loss: 0.2238\nEpoch 39/100\n60/60 [==============================] - 5s 89ms/step - loss: 0.1416 - val_loss: 0.2236\nEpoch 40/100\n60/60 [==============================] - 5s 88ms/step - loss: 0.1411 - val_loss: 0.2239\nEpoch 41/100\n60/60 [==============================] - 5s 88ms/step - loss: 0.1409 - val_loss: 0.2239\nEpoch 42/100\n60/60 [==============================] - 5s 88ms/step - loss: 0.1406 - val_loss: 0.2241\nEpoch 43/100\n60/60 [==============================] - 5s 88ms/step - loss: 0.1403 - val_loss: 0.2239\nEpoch 44/100\n60/60 [==============================] - 5s 89ms/step - loss: 0.1401 - val_loss: 0.2240\nEpoch 45/100\n60/60 [==============================] - 5s 90ms/step - loss: 0.1400 - val_loss: 0.2241\nEpoch 46/100\n60/60 [==============================] - 5s 89ms/step - loss: 0.1398 - val_loss: 0.2244\nEpoch 47/100\n60/60 [==============================] - 5s 90ms/step - loss: 0.1394 - val_loss: 0.2237\nEpoch 48/100\n60/60 [==============================] - 5s 90ms/step - loss: 0.1392 - val_loss: 0.2242\nEpoch 49/100\n60/60 [==============================] - 5s 90ms/step - loss: 0.1390 - val_loss: 0.2242\nEpoch 50/100\n60/60 [==============================] - 5s 88ms/step - loss: 0.1387 - val_loss: 0.2240\nEpoch 51/100\n60/60 [==============================] - 5s 88ms/step - loss: 0.1384 - val_loss: 0.2240\nEpoch 52/100\n60/60 [==============================] - 5s 89ms/step - loss: 0.1385 - val_loss: 0.2239\nEpoch 53/100\n60/60 [==============================] - 5s 88ms/step - loss: 0.1384 - val_loss: 0.2241\nEpoch 54/100\n60/60 [==============================] - 5s 88ms/step - loss: 0.1386 - val_loss: 0.2240\nEpoch 55/100\n60/60 [==============================] - 5s 89ms/step - loss: 0.1384 - val_loss: 0.2240\nEpoch 56/100\n60/60 [==============================] - 5s 89ms/step - loss: 0.1385 - val_loss: 0.2239\nEpoch 57/100\n60/60 [==============================] - 5s 89ms/step - loss: 0.1385 - val_loss: 0.2240\nEpoch 58/100\n60/60 [==============================] - 5s 89ms/step - loss: 0.1383 - val_loss: 0.2241\nEpoch 59/100\n60/60 [==============================] - 5s 88ms/step - loss: 0.1384 - val_loss: 0.2241\nEpoch 60/100\n60/60 [==============================] - 5s 88ms/step - loss: 0.1382 - val_loss: 0.2241\nEpoch 61/100\n60/60 [==============================] - 5s 88ms/step - loss: 0.1385 - val_loss: 0.2241\nEpoch 62/100\n60/60 [==============================] - 5s 88ms/step - loss: 0.1382 - val_loss: 0.2241\nEpoch 63/100\n60/60 [==============================] - 5s 88ms/step - loss: 0.1383 - val_loss: 0.2241\nEpoch 64/100\n60/60 [==============================] - 5s 89ms/step - loss: 0.1382 - val_loss: 0.2241\nEpoch 65/100\n60/60 [==============================] - 5s 88ms/step - loss: 0.1382 - val_loss: 0.2241\nEpoch 66/100\n60/60 [==============================] - 5s 89ms/step - loss: 0.1384 - val_loss: 0.2241\nEpoch 67/100\n60/60 [==============================] - 5s 89ms/step - loss: 0.1383 - val_loss: 0.2241\nEpoch 68/100\n60/60 [==============================] - 5s 89ms/step - loss: 0.1383 - val_loss: 0.2241\nEpoch 69/100\n60/60 [==============================] - 5s 88ms/step - loss: 0.1383 - val_loss: 0.2241\nEpoch 70/100\n60/60 [==============================] - 5s 87ms/step - loss: 0.1382 - val_loss: 0.2241\nEpoch 71/100\n60/60 [==============================] - 5s 89ms/step - loss: 0.1383 - val_loss: 0.2241\nEpoch 72/100\n60/60 [==============================] - 5s 89ms/step - loss: 0.1382 - val_loss: 0.2241\nEpoch 73/100\n60/60 [==============================] - 5s 88ms/step - loss: 0.1383 - val_loss: 0.2241\nEpoch 74/100\n60/60 [==============================] - 5s 88ms/step - loss: 0.1383 - val_loss: 0.2241\nEpoch 75/100\n60/60 [==============================] - 5s 88ms/step - loss: 0.1382 - val_loss: 0.2241\nEpoch 76/100\n60/60 [==============================] - 5s 90ms/step - loss: 0.1382 - val_loss: 0.2241\nEpoch 77/100\n60/60 [==============================] - 5s 89ms/step - loss: 0.1384 - val_loss: 0.2241\nEpoch 78/100\n60/60 [==============================] - 5s 89ms/step - loss: 0.1383 - val_loss: 0.2241\nEpoch 79/100\n60/60 [==============================] - 5s 88ms/step - loss: 0.1383 - val_loss: 0.2241\nEpoch 80/100\n60/60 [==============================] - 5s 89ms/step - loss: 0.1383 - val_loss: 0.2241\nEpoch 81/100\n60/60 [==============================] - 5s 89ms/step - loss: 0.1382 - val_loss: 0.2241\nEpoch 82/100\n","name":"stdout"},{"output_type":"stream","text":"60/60 [==============================] - 5s 90ms/step - loss: 0.1383 - val_loss: 0.2241\nEpoch 83/100\n60/60 [==============================] - 5s 89ms/step - loss: 0.1383 - val_loss: 0.2241\nEpoch 84/100\n60/60 [==============================] - 5s 88ms/step - loss: 0.1384 - val_loss: 0.2241\nEpoch 85/100\n60/60 [==============================] - 5s 89ms/step - loss: 0.1384 - val_loss: 0.2241\nEpoch 86/100\n60/60 [==============================] - 5s 88ms/step - loss: 0.1383 - val_loss: 0.2241\nEpoch 87/100\n60/60 [==============================] - 5s 88ms/step - loss: 0.1382 - val_loss: 0.2241\nEpoch 88/100\n60/60 [==============================] - 5s 90ms/step - loss: 0.1384 - val_loss: 0.2241\nEpoch 89/100\n60/60 [==============================] - 5s 89ms/step - loss: 0.1382 - val_loss: 0.2241\nEpoch 90/100\n60/60 [==============================] - 5s 89ms/step - loss: 0.1384 - val_loss: 0.2241\nEpoch 91/100\n60/60 [==============================] - 5s 89ms/step - loss: 0.1383 - val_loss: 0.2241\nEpoch 92/100\n60/60 [==============================] - 5s 88ms/step - loss: 0.1383 - val_loss: 0.2241\nEpoch 93/100\n60/60 [==============================] - 5s 89ms/step - loss: 0.1383 - val_loss: 0.2241\nEpoch 94/100\n60/60 [==============================] - 5s 89ms/step - loss: 0.1383 - val_loss: 0.2241\nEpoch 95/100\n60/60 [==============================] - 5s 88ms/step - loss: 0.1384 - val_loss: 0.2241\nEpoch 96/100\n60/60 [==============================] - 5s 88ms/step - loss: 0.1383 - val_loss: 0.2241\nEpoch 97/100\n60/60 [==============================] - 5s 88ms/step - loss: 0.1383 - val_loss: 0.2241\nEpoch 98/100\n60/60 [==============================] - 5s 88ms/step - loss: 0.1384 - val_loss: 0.2241\nEpoch 99/100\n38/60 [==================>...........] - ETA: 1s - loss: 0.1381","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-12-c9d17c4d227d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mnmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m     \u001b[0mholdouts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mholdout_preds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpublic_df\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpublic_preds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprivate_df\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprivate_preds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_and_predict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\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      8\u001b[0m     \u001b[0mval_df\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mholdouts\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0mval_preds\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mholdout_preds\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m<ipython-input-11-caafc60a421e>\u001b[0m in \u001b[0;36mtrain_and_predict\u001b[0;34m(type, FOLD_N)\u001b[0m\n\u001b[1;32m     37\u001b[0m             callbacks=[\n\u001b[1;32m     38\u001b[0m                 \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mReduceLROnPlateau\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 39\u001b[0;31m                 \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mModelCheckpoint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'model{Ver}_cv{cv}.h5'\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     40\u001b[0m             ]\n\u001b[1;32m     41\u001b[0m         )\n","\u001b[0;32m/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36m_method_wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    106\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_method_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    107\u001b[0m     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already.\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/python3.7/site-packages/tensorflow/python/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, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1096\u001b[0m                 batch_size=batch_size):\n\u001b[1;32m   1097\u001b[0m               \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1098\u001b[0;31m               \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m 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\u001b[0;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    806\u001b[0m       \u001b[0;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 807\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateless_fn\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[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    808\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m 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cancellation_manager)\u001b[0m\n\u001b[1;32m   1846\u001b[0m                            resource_variable_ops.BaseResourceVariable))],\n\u001b[1;32m   1847\u001b[0m         \u001b[0mcaptured_inputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcaptured_inputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1848\u001b[0;31m         cancellation_manager=cancellation_manager)\n\u001b[0m\u001b[1;32m   1849\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1850\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_call_flat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcaptured_inputs\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0margs\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/python3.7/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    548\u001b[0m               \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    549\u001b[0m               \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 550\u001b[0;31m               ctx=ctx)\n\u001b[0m\u001b[1;32m    551\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    552\u001b[0m           outputs = execute.execute_with_cancellation(\n","\u001b[0;32m/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     58\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0;32m---> 60\u001b[0;31m                                         inputs, attrs, num_outputs)\n\u001b[0m\u001b[1;32m     61\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\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;31mKeyboardInterrupt\u001b[0m: "]}]},{"metadata":{"papermill":{"duration":12.712813,"end_time":"2020-09-21T09:56:00.094415","exception":false,"start_time":"2020-09-21T09:55:47.381602","status":"completed"},"tags":[]},"cell_type":"markdown","source":"## post process"},{"metadata":{"execution":{"iopub.execute_input":"2020-09-21T09:56:26.121627Z","iopub.status.busy":"2020-09-21T09:56:26.08081Z","iopub.status.idle":"2020-09-21T09:57:04.897064Z","shell.execute_reply":"2020-09-21T09:57:04.897651Z"},"papermill":{"duration":51.76575,"end_time":"2020-09-21T09:57:04.897814","exception":false,"start_time":"2020-09-21T09:56:13.132064","status":"completed"},"tags":[],"trusted":true},"cell_type":"code","source":"preds_ls = []\nfor df, preds in zip(test_df, test_preds):\n    for i, uid in enumerate(df.id):\n        single_pred = preds[i]\n        single_df = pd.DataFrame(single_pred, columns=pred_cols)\n        single_df['id_seqpos'] = [f'{uid}_{x}' for x in range(single_df.shape[0])]\n        preds_ls.append(single_df)\npreds_df = pd.concat(preds_ls).groupby('id_seqpos').mean().reset_index()\n# .mean() is for\n# 1, Predictions from multiple models\n# 2, TTA (augmented test data)\n\npreds_ls = []\nfor df, preds in zip(val_df, val_preds):\n    for i, uid in enumerate(df.id):\n        single_pred = preds[i]\n        single_df = pd.DataFrame(single_pred, columns=pred_cols)\n        single_df['id_seqpos'] = [f'{uid}_{x}' for x in range(single_df.shape[0])]\n        single_df['SN_filter'] = df[df['id'] == uid].SN_filter.values[0]\n        preds_ls.append(single_df)\nholdouts_df = pd.concat(preds_ls).groupby('id_seqpos').mean().reset_index()","execution_count":null,"outputs":[]},{"metadata":{"execution":{"iopub.execute_input":"2020-09-21T09:57:30.84898Z","iopub.status.busy":"2020-09-21T09:57:30.848097Z","iopub.status.idle":"2020-09-21T09:57:35.396342Z","shell.execute_reply":"2020-09-21T09:57:35.397025Z"},"papermill":{"duration":17.293127,"end_time":"2020-09-21T09:57:35.397197","exception":false,"start_time":"2020-09-21T09:57:18.10407","status":"completed"},"tags":[],"trusted":true},"cell_type":"code","source":"submission = preds_df[['id_seqpos', 'reactivity', 'deg_Mg_pH10', 'deg_pH10', 'deg_Mg_50C', 'deg_50C']]\nsubmission.to_csv(f'submission.csv', index=False)\nprint(f'wrote to submission.csv')","execution_count":null,"outputs":[]},{"metadata":{"papermill":{"duration":12.384864,"end_time":"2020-09-21T09:58:00.228947","exception":false,"start_time":"2020-09-21T09:57:47.844083","status":"completed"},"tags":[]},"cell_type":"markdown","source":"## Validation"},{"metadata":{"execution":{"iopub.execute_input":"2020-09-21T09:58:26.104504Z","iopub.status.busy":"2020-09-21T09:58:26.103111Z","iopub.status.idle":"2020-09-21T09:58:26.105315Z","shell.execute_reply":"2020-09-21T09:58:26.106353Z"},"papermill":{"duration":12.82694,"end_time":"2020-09-21T09:58:26.106501","exception":false,"start_time":"2020-09-21T09:58:13.279561","status":"completed"},"tags":[],"trusted":true},"cell_type":"code","source":"def print_mse(prd):\n    val = pd.read_json('../input/stanford-covid-vaccine/train.json', lines=True)\n\n    val_data = []\n    for mol_id in val['id'].unique():\n        sample_data = val.loc[val['id'] == mol_id]\n        sample_seq_length = sample_data.seq_length.values[0]\n        for i in range(68):\n            sample_dict = {\n                           'id_seqpos' : sample_data['id'].values[0] + '_' + str(i),\n                           'reactivity_gt' : sample_data['reactivity'].values[0][i],\n                           'deg_Mg_pH10_gt' : sample_data['deg_Mg_pH10'].values[0][i],\n                           'deg_Mg_50C_gt' : sample_data['deg_Mg_50C'].values[0][i],\n                           }\n            val_data.append(sample_dict)\n    val_data = pd.DataFrame(val_data)\n    val_data = val_data.merge(prd, on='id_seqpos')\n\n    rmses = []\n    mses = []\n    for col in ['reactivity', 'deg_Mg_pH10', 'deg_Mg_50C']:\n        rmse = ((val_data[col] - val_data[col+'_gt']) ** 2).mean() ** .5\n        mse = ((val_data[col] - val_data[col+'_gt']) ** 2).mean()\n        rmses.append(rmse)\n        mses.append(mse)\n        print(col, rmse, mse)\n    print(np.mean(rmses), np.mean(mses))","execution_count":null,"outputs":[]},{"metadata":{"execution":{"iopub.execute_input":"2020-09-21T09:58:51.307542Z","iopub.status.busy":"2020-09-21T09:58:51.306918Z","iopub.status.idle":"2020-09-21T09:59:14.103528Z","shell.execute_reply":"2020-09-21T09:59:14.105346Z"},"papermill":{"duration":35.353703,"end_time":"2020-09-21T09:59:14.105565","exception":false,"start_time":"2020-09-21T09:58:38.751862","status":"completed"},"tags":[],"trusted":true},"cell_type":"code","source":"print_mse(holdouts_df)","execution_count":null,"outputs":[]},{"metadata":{"execution":{"iopub.execute_input":"2020-09-21T09:59:39.272921Z","iopub.status.busy":"2020-09-21T09:59:39.271903Z","iopub.status.idle":"2020-09-21T09:59:46.041567Z","shell.execute_reply":"2020-09-21T09:59:46.040925Z"},"papermill":{"duration":19.171895,"end_time":"2020-09-21T09:59:46.041676","exception":false,"start_time":"2020-09-21T09:59:26.869781","status":"completed"},"tags":[],"trusted":true},"cell_type":"code","source":"print_mse(holdouts_df[holdouts_df.SN_filter == 1])","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.7.6","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":4}