{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "6b84fefd-5936-4da4-ab6b-5b944329ad1d", "metadata": {}, "outputs": [], "source": [ "import os\n", "os.environ['CUDA_DEVICE_ORDER'] = 'PCB_BUS_ID'\n", "os.environ['CUDA_VISIBLE_DEVICES'] = '0, 1'" ] }, { "cell_type": "code", "execution_count": 2, "id": "9cf130e3-62ef-46e0-bbdc-b13d9d29318d", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "from sklearn.model_selection import train_test_split\n", "import matplotlib.pyplot as plt\n", "#新增加的两行\n", "from pylab import mpl\n", "# 设置显示中文字体\n", "mpl.rcParams[\"font.sans-serif\"] = [\"SimHei\"]\n", "\n", "mpl.rcParams[\"axes.unicode_minus\"] = False" ] }, { "cell_type": "code", "execution_count": 3, "id": "752381a5-0aeb-4c54-bc48-f9c3f8fc5d17", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Unnamed: 0_level_0氢碳氮氧弹筒发热量挥发分固定炭
化验编号HadCadNadOadQb,adVadFcad
Unnamed: 0_level_2(%)(%)(%)(%)MJ/kg(%)(%)
027201105293.9370.180.8125.07927.82032.0655.68
127200968833.7868.930.7726.51227.40429.9654.71
227201090843.4869.600.7626.14827.57829.3155.99
327200847083.4766.710.7629.05526.33828.5853.87
427200627213.8768.780.8026.54227.28029.9754.78
...........................
22327200304904.1268.850.9726.05527.86432.9451.89
22427200286333.9767.040.9428.04327.36831.8851.38
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228 rows × 8 columns

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" ], "text/plain": [ " Unnamed: 0_level_0 氢 碳 氮 氧 弹筒发热量 挥发分 固定炭\n", " 化验编号 Had Cad Nad Oad Qb,ad Vad Fcad\n", " Unnamed: 0_level_2 (%) (%) (%) (%) MJ/kg (%) (%)\n", "0 2720110529 3.93 70.18 0.81 25.079 27.820 32.06 55.68\n", "1 2720096883 3.78 68.93 0.77 26.512 27.404 29.96 54.71\n", "2 2720109084 3.48 69.60 0.76 26.148 27.578 29.31 55.99\n", "3 2720084708 3.47 66.71 0.76 29.055 26.338 28.58 53.87\n", "4 2720062721 3.87 68.78 0.80 26.542 27.280 29.97 54.78\n", ".. ... ... ... ... ... ... ... ...\n", "223 2720030490 4.12 68.85 0.97 26.055 27.864 32.94 51.89\n", "224 2720028633 3.97 67.04 0.94 28.043 27.368 31.88 51.38\n", "225 2720028634 4.12 68.42 0.96 26.493 27.886 33.16 52.00\n", "226 2720017683 3.88 67.42 0.94 27.760 26.616 31.65 50.56\n", "227 2720017678 3.81 66.74 0.92 28.530 26.688 31.02 50.82\n", "\n", "[228 rows x 8 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_0102 = pd.read_excel('./data/20240102/20240102.xlsx', header=[0,1,2])\n", "data_0102" ] }, { "cell_type": "code", "execution_count": 4, "id": "972f1e9c-3ebc-45cf-8d1f-7611645e5238", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['化验编号',\n", " '氢Had(%)',\n", " '碳Cad(%)',\n", " '氮Nad(%)',\n", " '氧Oad(%)',\n", " '弹筒发热量Qb,adMJ/kg',\n", " '挥发分Vad(%)',\n", " '固定炭Fcad(%)']" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cols = [''.join([y for y in x if 'Unnamed' not in y]) for x in data_0102.columns]\n", "cols" ] }, { "cell_type": "code", "execution_count": 5, "id": "c95f1106-b3a4-43c6-88ec-3cdebf91d79a", "metadata": {}, "outputs": [], "source": [ "data_0102.columns = cols" ] }, { "cell_type": "code", "execution_count": 6, "id": "2e96af0a-feda-4a1f-a13e-9c8861c6f4d4", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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化验编号氢Had(%)碳Cad(%)氮Nad(%)氧Oad(%)弹筒发热量Qb,adMJ/kg挥发分Vad(%)固定炭Fcad(%)
027201105293.9370.180.8125.07927.82032.0655.68
127200968833.7868.930.7726.51227.40429.9654.71
227201090843.4869.600.7626.14827.57829.3155.99
327200847083.4766.710.7629.05526.33828.5853.87
427200627213.8768.780.8026.54227.28029.9754.78
...........................
22327200304904.1268.850.9726.05527.86432.9451.89
22427200286333.9767.040.9428.04327.36831.8851.38
22527200286344.1268.420.9626.49327.88633.1652.00
22627200176833.8867.420.9427.76026.61631.6550.56
22727200176783.8166.740.9228.53026.68831.0250.82
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228 rows × 8 columns

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" ], "text/plain": [ " 化验编号 氢Had(%) 碳Cad(%) 氮Nad(%) 氧Oad(%) 弹筒发热量Qb,adMJ/kg \\\n", "0 2720110529 3.93 70.18 0.81 25.079 27.820 \n", "1 2720096883 3.78 68.93 0.77 26.512 27.404 \n", "2 2720109084 3.48 69.60 0.76 26.148 27.578 \n", "3 2720084708 3.47 66.71 0.76 29.055 26.338 \n", "4 2720062721 3.87 68.78 0.80 26.542 27.280 \n", ".. ... ... ... ... ... ... \n", "223 2720030490 4.12 68.85 0.97 26.055 27.864 \n", "224 2720028633 3.97 67.04 0.94 28.043 27.368 \n", "225 2720028634 4.12 68.42 0.96 26.493 27.886 \n", "226 2720017683 3.88 67.42 0.94 27.760 26.616 \n", "227 2720017678 3.81 66.74 0.92 28.530 26.688 \n", "\n", " 挥发分Vad(%) 固定炭Fcad(%) \n", "0 32.06 55.68 \n", "1 29.96 54.71 \n", "2 29.31 55.99 \n", "3 28.58 53.87 \n", "4 29.97 54.78 \n", ".. ... ... \n", "223 32.94 51.89 \n", "224 31.88 51.38 \n", "225 33.16 52.00 \n", "226 31.65 50.56 \n", "227 31.02 50.82 \n", "\n", "[228 rows x 8 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_0102" ] }, { "cell_type": "code", "execution_count": 7, "id": "04b177a7-2f02-4e23-8ea9-29f34cf3eafc", "metadata": {}, "outputs": [], "source": [ "out_cols = ['挥发分Vad(%)', '固定炭Fcad(%)']" ] }, { "cell_type": "code", "execution_count": 8, "id": "31169fbf-d78e-42f7-87f3-71ba3dd0979d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['挥发分Vad(%)', '固定炭Fcad(%)']" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "out_cols" ] }, { "cell_type": "code", "execution_count": 9, "id": "feaedd50-f999-45bf-b465-3d359b0c0110", "metadata": {}, "outputs": [], "source": [ "data = data_0102.copy()" ] }, { "cell_type": "code", "execution_count": 10, "id": "a40bee0f-011a-4edb-80f8-4e2f40e755fd", "metadata": {}, "outputs": [], "source": [ "train_data = data.dropna(subset=out_cols).fillna(0)" ] }, { "cell_type": "code", "execution_count": 11, "id": "535d37b6-b9de-4025-ac8f-62f5bdbe2451", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2024-01-04 16:49:03.492957: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n" ] } ], "source": [ "import tensorflow as tf\n", "from tensorflow import keras\n", "from tensorflow.keras import layers\n", "import tensorflow.keras.backend as K" ] }, { "cell_type": "code", "execution_count": 12, "id": "c2318ce6-60d2-495c-91cd-67ca53609cf8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:From /tmp/ipykernel_45930/337460670.py:1: is_gpu_available (from tensorflow.python.framework.test_util) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "Use `tf.config.list_physical_devices('GPU')` instead.\n" ] }, { "data": { "text/plain": [ "False" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "name": "stderr", "output_type": "stream", "text": [ "2024-01-04 16:49:04.396035: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F FMA\n", "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", "2024-01-04 16:49:04.407586: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcuda.so.1\n", "2024-01-04 16:49:04.465739: E tensorflow/stream_executor/cuda/cuda_driver.cc:328] failed call to cuInit: CUDA_ERROR_INVALID_DEVICE: invalid device ordinal\n", "2024-01-04 16:49:04.465795: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:169] retrieving CUDA diagnostic information for host: zhaojh-yv621\n", "2024-01-04 16:49:04.465807: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:176] hostname: zhaojh-yv621\n", "2024-01-04 16:49:04.466010: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:200] libcuda reported version is: 520.61.5\n", "2024-01-04 16:49:04.466041: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:204] kernel reported version is: 520.61.5\n", "2024-01-04 16:49:04.466045: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:310] kernel version seems to match DSO: 520.61.5\n" ] } ], "source": [ "tf.test.is_gpu_available()" ] }, { "cell_type": "code", "execution_count": 13, "id": "1c85d462-f248-4ffb-908f-eb4b20eab179", "metadata": {}, "outputs": [], "source": [ "class TransformerBlock(layers.Layer):\n", " def __init__(self, embed_dim, num_heads, ff_dim, name, rate=0.1):\n", " super().__init__()\n", " self.att = layers.MultiHeadAttention(num_heads=num_heads, key_dim=embed_dim, name=name)\n", " self.ffn = keras.Sequential(\n", " [layers.Dense(ff_dim, activation=\"relu\"), layers.Dense(embed_dim),]\n", " )\n", " self.layernorm1 = layers.LayerNormalization(epsilon=1e-6)\n", " self.layernorm2 = layers.LayerNormalization(epsilon=1e-6)\n", " self.dropout1 = layers.Dropout(rate)\n", " self.dropout2 = layers.Dropout(rate)\n", "\n", " def call(self, inputs, training):\n", " attn_output = self.att(inputs, inputs)\n", " attn_output = self.dropout1(attn_output, training=training)\n", " out1 = self.layernorm1(inputs + attn_output)\n", " ffn_output = self.ffn(out1)\n", " ffn_output = self.dropout2(ffn_output, training=training)\n", " return self.layernorm2(out1 + ffn_output)" ] }, { "cell_type": "code", "execution_count": 14, "id": "790284a3-b9d3-4144-b481-38a7c3ecb4b9", "metadata": {}, "outputs": [], "source": [ "from tensorflow.keras import Model" ] }, { "cell_type": "code", "execution_count": 15, "id": "cd9a1ca1-d0ca-4cb5-9ef5-fd5d63576cd2", "metadata": {}, "outputs": [], "source": [ "from tensorflow.keras.initializers import Constant" ] }, { "cell_type": "code", "execution_count": 16, "id": "9bc02f29-0fb7-420d-99a8-435eadc06e29", "metadata": {}, "outputs": [], "source": [ "# Custom loss layer\n", "class CustomMultiLossLayer(layers.Layer):\n", " def __init__(self, nb_outputs=2, **kwargs):\n", " self.nb_outputs = nb_outputs\n", " self.is_placeholder = True\n", " super(CustomMultiLossLayer, self).__init__(**kwargs)\n", " \n", " def build(self, input_shape=None):\n", " # initialise log_vars\n", " self.log_vars = []\n", " for i in range(self.nb_outputs):\n", " self.log_vars += [self.add_weight(name='log_var' + str(i), shape=(1,),\n", " initializer=tf.initializers.he_normal(), trainable=True)]\n", " super(CustomMultiLossLayer, self).build(input_shape)\n", "\n", " def multi_loss(self, ys_true, ys_pred):\n", " assert len(ys_true) == self.nb_outputs and len(ys_pred) == self.nb_outputs\n", " loss = 0\n", " for y_true, y_pred, log_var in zip(ys_true, ys_pred, self.log_vars):\n", " mse = (y_true - y_pred) ** 2.\n", " pre = K.exp(-log_var[0])\n", " loss += tf.abs(tf.reduce_logsumexp(pre * mse + log_var[0], axis=-1))\n", " return K.mean(loss)\n", "\n", " def call(self, inputs):\n", " ys_true = inputs[:self.nb_outputs]\n", " ys_pred = inputs[self.nb_outputs:]\n", " loss = self.multi_loss(ys_true, ys_pred)\n", " self.add_loss(loss, inputs=inputs)\n", " # We won't actually use the output.\n", " return K.concatenate(inputs, -1)" ] }, { "cell_type": "code", "execution_count": 17, "id": "a190207e-5a59-4813-9660-758760cf1b73", "metadata": {}, "outputs": [], "source": [ "num_heads, ff_dim = 3, 16" ] }, { "cell_type": "code", "execution_count": 18, "id": "80f32155-e71f-4615-8d0c-01dfd04988fe", "metadata": {}, "outputs": [], "source": [ "def get_prediction_model():\n", " def build_output(out, out_name):\n", " self_block = TransformerBlock(64, num_heads, ff_dim, name=f'{out_name}_attn')\n", " out = self_block(out)\n", " out = layers.GlobalAveragePooling1D()(out)\n", " out = layers.Dropout(0.1)(out)\n", " out = layers.Dense(32, activation=\"relu\")(out)\n", " # out = layers.Dense(1, name=out_name, activation=\"sigmoid\")(out)\n", " return out\n", " inputs = layers.Input(shape=(1,len(feature_cols)), name='input')\n", " x = layers.Conv1D(filters=64, kernel_size=1, activation='relu')(inputs)\n", " # x = layers.Dropout(rate=0.1)(x)\n", " lstm_out = layers.Bidirectional(layers.LSTM(units=64, return_sequences=True))(x)\n", " lstm_out = layers.Dense(128, activation='relu')(lstm_out)\n", " transformer_block = TransformerBlock(128, num_heads, ff_dim, name='first_attn')\n", " out = transformer_block(lstm_out)\n", " out = layers.GlobalAveragePooling1D()(out)\n", " out = layers.Dropout(0.1)(out)\n", " out = layers.Dense(64, activation='relu')(out)\n", " out = K.expand_dims(out, axis=1)\n", "\n", " bet = build_output(out, 'vad')\n", " mesco = build_output(out, 'fcad')\n", "\n", " bet = layers.Dense(1, activation='sigmoid', name='vad')(bet)\n", " mesco = layers.Dense(1, activation='sigmoid', name='fcad')(mesco)\n", "\n", " model = Model(inputs=[inputs], outputs=[bet, mesco])\n", " return model\n" ] }, { "cell_type": "code", "execution_count": 30, "id": "264001b1-5e4a-4786-96fd-2b5c70ab3212", "metadata": {}, "outputs": [], "source": [ "def get_trainable_model(prediction_model):\n", " inputs = layers.Input(shape=(1,len(feature_cols)), name='input')\n", " bet, mesco = prediction_model(inputs)\n", " bet_real = layers.Input(shape=(1,), name='vad_real')\n", " mesco_real = layers.Input(shape=(1,), name='fcad_real')\n", " out = CustomMultiLossLayer(nb_outputs=2)([bet_real, mesco_real, bet, mesco])\n", " return Model([inputs, bet_real, mesco_real], out)" ] }, { "cell_type": "code", "execution_count": 20, "id": "1eebdab3-1f88-48a1-b5e0-bc8787528c1b", "metadata": {}, "outputs": [], "source": [ "maxs = train_data.max()\n", "mins = train_data.min()\n", "for col in train_data.columns:\n", " if maxs[col] - mins[col] == 0:\n", " continue\n", " train_data[col] = (train_data[col] - mins[col]) / (maxs[col] - mins[col])" ] }, { "cell_type": "code", "execution_count": 21, "id": "7f27bd56-4f6b-4242-9f79-c7d6b3ee2f13", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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化验编号氢Had(%)碳Cad(%)氮Nad(%)氧Oad(%)弹筒发热量Qb,adMJ/kg挥发分Vad(%)固定炭Fcad(%)
00.9965470.7739730.8354140.4565220.1714630.8112490.8477370.828147
10.8511180.6712330.7999430.3695650.2102540.7820380.6748970.794606
20.9811470.4657530.8189560.3478260.2004010.7942560.6213990.838866
30.7213670.4589040.7369470.3478260.2790940.7071830.5613170.765560
40.4870460.7328770.7956870.4347830.2110660.7733310.6757200.797026
...........................
2230.1435530.9041100.7976730.8043480.1978830.8143390.9201650.697095
2240.1237620.8013700.7463110.7391300.2516990.7795100.8329220.679461
2250.1237730.9041100.7854710.7826090.2097400.8158840.9382720.700899
2260.0070660.7397260.7570940.7391300.2440380.7267050.8139920.651107
2270.0070120.6917810.7377980.6956520.2648820.7317600.7621400.660097
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228 rows × 8 columns

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" ], "text/plain": [ " 化验编号 氢Had(%) 碳Cad(%) 氮Nad(%) 氧Oad(%) 弹筒发热量Qb,adMJ/kg \\\n", "0 0.996547 0.773973 0.835414 0.456522 0.171463 0.811249 \n", "1 0.851118 0.671233 0.799943 0.369565 0.210254 0.782038 \n", "2 0.981147 0.465753 0.818956 0.347826 0.200401 0.794256 \n", "3 0.721367 0.458904 0.736947 0.347826 0.279094 0.707183 \n", "4 0.487046 0.732877 0.795687 0.434783 0.211066 0.773331 \n", ".. ... ... ... ... ... ... \n", "223 0.143553 0.904110 0.797673 0.804348 0.197883 0.814339 \n", "224 0.123762 0.801370 0.746311 0.739130 0.251699 0.779510 \n", "225 0.123773 0.904110 0.785471 0.782609 0.209740 0.815884 \n", "226 0.007066 0.739726 0.757094 0.739130 0.244038 0.726705 \n", "227 0.007012 0.691781 0.737798 0.695652 0.264882 0.731760 \n", "\n", " 挥发分Vad(%) 固定炭Fcad(%) \n", "0 0.847737 0.828147 \n", "1 0.674897 0.794606 \n", "2 0.621399 0.838866 \n", "3 0.561317 0.765560 \n", "4 0.675720 0.797026 \n", ".. ... ... \n", "223 0.920165 0.697095 \n", "224 0.832922 0.679461 \n", "225 0.938272 0.700899 \n", "226 0.813992 0.651107 \n", "227 0.762140 0.660097 \n", "\n", "[228 rows x 8 columns]" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_data" ] }, { "cell_type": "code", "execution_count": 22, "id": "baf45a3d-dc01-44fc-9f0b-456964ac2cdb", "metadata": {}, "outputs": [], "source": [ "# feature_cols = [x for x in train_data.columns if x not in out_cols and '第二次' not in x]\n", "feature_cols = [x for x in train_data.columns if x not in out_cols]\n", "use_cols = feature_cols + out_cols" ] }, { "cell_type": "code", "execution_count": 23, "id": "f2d27538-d2bc-4202-b0cf-d3e0949b4686", "metadata": {}, "outputs": [], "source": [ "use_data = train_data.copy()\n", "for col in use_cols:\n", " use_data[col] = use_data[col].astype('float32')" ] }, { "cell_type": "code", "execution_count": 24, "id": "54c1df2c-c297-4b8d-be8a-3a99cff22545", "metadata": {}, "outputs": [], "source": [ "train, valid = train_test_split(use_data[use_cols], test_size=0.3, random_state=42, shuffle=True)\n", "valid, test = train_test_split(valid, test_size=0.3, random_state=42, shuffle=True)" ] }, { "cell_type": "code", "execution_count": 31, "id": "e7a914da-b9c2-40d9-96e0-459b0888adba", "metadata": {}, "outputs": [], "source": [ "prediction_model = get_prediction_model()\n", "trainable_model = get_trainable_model(prediction_model)" ] }, { "cell_type": "code", "execution_count": 32, "id": "4f832a1e-48e2-4467-b381-35b9d2f1271a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model: \"model_3\"\n", "__________________________________________________________________________________________________\n", "Layer (type) Output Shape Param # Connected to \n", "==================================================================================================\n", "input (InputLayer) [(None, 1, 6)] 0 \n", "__________________________________________________________________________________________________\n", "conv1d_3 (Conv1D) (None, 1, 64) 448 input[0][0] \n", "__________________________________________________________________________________________________\n", "bidirectional_3 (Bidirectional) (None, 1, 128) 66048 conv1d_3[0][0] \n", "__________________________________________________________________________________________________\n", "dense_30 (Dense) (None, 1, 128) 16512 bidirectional_3[0][0] \n", "__________________________________________________________________________________________________\n", "transformer_block_9 (Transforme (None, 1, 128) 202640 dense_30[0][0] \n", "__________________________________________________________________________________________________\n", "global_average_pooling1d_9 (Glo (None, 128) 0 transformer_block_9[0][0] \n", "__________________________________________________________________________________________________\n", "dropout_29 (Dropout) (None, 128) 0 global_average_pooling1d_9[0][0] \n", "__________________________________________________________________________________________________\n", "dense_33 (Dense) (None, 64) 8256 dropout_29[0][0] \n", "__________________________________________________________________________________________________\n", "tf.expand_dims_3 (TFOpLambda) (None, 1, 64) 0 dense_33[0][0] \n", "__________________________________________________________________________________________________\n", "transformer_block_10 (Transform (None, 1, 64) 52176 tf.expand_dims_3[0][0] \n", "__________________________________________________________________________________________________\n", "transformer_block_11 (Transform (None, 1, 64) 52176 tf.expand_dims_3[0][0] \n", "__________________________________________________________________________________________________\n", "global_average_pooling1d_10 (Gl (None, 64) 0 transformer_block_10[0][0] \n", "__________________________________________________________________________________________________\n", "global_average_pooling1d_11 (Gl (None, 64) 0 transformer_block_11[0][0] \n", "__________________________________________________________________________________________________\n", "dropout_32 (Dropout) (None, 64) 0 global_average_pooling1d_10[0][0]\n", "__________________________________________________________________________________________________\n", "dropout_35 (Dropout) (None, 64) 0 global_average_pooling1d_11[0][0]\n", "__________________________________________________________________________________________________\n", "dense_36 (Dense) (None, 32) 2080 dropout_32[0][0] \n", "__________________________________________________________________________________________________\n", "dense_39 (Dense) (None, 32) 2080 dropout_35[0][0] \n", "__________________________________________________________________________________________________\n", "vad (Dense) (None, 1) 33 dense_36[0][0] \n", "__________________________________________________________________________________________________\n", "fcad (Dense) (None, 1) 33 dense_39[0][0] \n", "==================================================================================================\n", "Total params: 402,482\n", "Trainable params: 402,482\n", "Non-trainable params: 0\n", "__________________________________________________________________________________________________\n" ] } ], "source": [ "prediction_model.summary()" ] }, { "cell_type": "code", "execution_count": 33, "id": "9289f452-a5a4-40c4-b942-f6cb2e348548", "metadata": {}, "outputs": [], "source": [ "from tensorflow.keras import optimizers\n", "from tensorflow.python.keras.utils.vis_utils import plot_model" ] }, { "cell_type": "code", "execution_count": 34, "id": "2494ef5a-5b2b-4f11-b6cd-dc39503c9106", "metadata": {}, "outputs": [], "source": [ "X = np.expand_dims(train[feature_cols].values, axis=1)\n", "Y = [x for x in train[out_cols].values.T]\n", "Y_valid = [x for x in valid[out_cols].values.T]" ] }, { "cell_type": "code", "execution_count": 35, "id": "9a62dea1-4f05-411b-9756-a91623580581", "metadata": {}, "outputs": [], "source": [ "from keras.callbacks import ReduceLROnPlateau\n", "reduce_lr = ReduceLROnPlateau(monitor='val_loss', patience=10, mode='auto')" ] }, { "cell_type": "code", "execution_count": 40, "id": "cf869e4d-0fce-45a2-afff-46fd9b30fd1c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/120\n", "20/20 [==============================] - 5s 59ms/step - loss: 1.8316 - val_loss: 1.8096\n", "Epoch 2/120\n", "20/20 [==============================] - 0s 25ms/step - loss: 1.7903 - val_loss: 1.7691\n", "Epoch 3/120\n", "20/20 [==============================] - 0s 26ms/step - loss: 1.7506 - val_loss: 1.7307\n", "Epoch 4/120\n", "20/20 [==============================] - 0s 26ms/step - loss: 1.7110 - val_loss: 1.6914\n", "Epoch 5/120\n", "20/20 [==============================] - 0s 21ms/step - loss: 1.6711 - val_loss: 1.6497\n", "Epoch 6/120\n", "20/20 [==============================] - 0s 24ms/step - loss: 1.6314 - val_loss: 1.6098\n", "Epoch 7/120\n", "20/20 [==============================] - 0s 24ms/step - loss: 1.5909 - val_loss: 1.5695\n", "Epoch 8/120\n", "20/20 [==============================] - 0s 24ms/step - loss: 1.5506 - val_loss: 1.5296\n", "Epoch 9/120\n", "20/20 [==============================] - 0s 24ms/step - loss: 1.5109 - val_loss: 1.4891\n", "Epoch 10/120\n", "20/20 [==============================] - 0s 25ms/step - loss: 1.4706 - val_loss: 1.4500\n", "Epoch 11/120\n", "20/20 [==============================] - 0s 25ms/step - loss: 1.4306 - val_loss: 1.4104\n", "Epoch 12/120\n", "20/20 [==============================] - 0s 22ms/step - loss: 1.3907 - val_loss: 1.3746\n", "Epoch 13/120\n", "20/20 [==============================] - 0s 23ms/step - loss: 1.3508 - val_loss: 1.3296\n", "Epoch 14/120\n", "20/20 [==============================] - 0s 26ms/step - loss: 1.3106 - val_loss: 1.2895\n", "Epoch 15/120\n", "20/20 [==============================] - 0s 26ms/step - loss: 1.2706 - val_loss: 1.2515\n", "Epoch 16/120\n", "20/20 [==============================] - 0s 22ms/step - loss: 1.2315 - val_loss: 1.2104\n", "Epoch 17/120\n", "20/20 [==============================] - 0s 23ms/step - loss: 1.1908 - val_loss: 1.1702\n", "Epoch 18/120\n", "20/20 [==============================] - 0s 25ms/step - loss: 1.1508 - val_loss: 1.1320\n", "Epoch 19/120\n", "20/20 [==============================] - 0s 26ms/step - loss: 1.1114 - val_loss: 1.0917\n", "Epoch 20/120\n", "20/20 [==============================] - 0s 24ms/step - loss: 1.0718 - val_loss: 1.0513\n", "Epoch 21/120\n", "20/20 [==============================] - 0s 23ms/step - loss: 1.0315 - val_loss: 1.0178\n", "Epoch 22/120\n", "20/20 [==============================] - 0s 24ms/step - loss: 0.9918 - val_loss: 0.9704\n", "Epoch 23/120\n", "20/20 [==============================] - 0s 23ms/step - loss: 0.9511 - val_loss: 0.9321\n", "Epoch 24/120\n", "20/20 [==============================] - 0s 26ms/step - loss: 0.9114 - val_loss: 0.8913\n", "Epoch 25/120\n", "20/20 [==============================] - 0s 26ms/step - loss: 0.8718 - val_loss: 0.8520\n", "Epoch 26/120\n", "20/20 [==============================] - 0s 25ms/step - loss: 0.8314 - val_loss: 0.8124\n", "Epoch 27/120\n", "20/20 [==============================] - 0s 26ms/step - loss: 0.7922 - val_loss: 0.7727\n", "Epoch 28/120\n", "20/20 [==============================] - 0s 25ms/step - loss: 0.7519 - val_loss: 0.7307\n", "Epoch 29/120\n", "20/20 [==============================] - 0s 26ms/step - loss: 0.7119 - val_loss: 0.6932\n", "Epoch 30/120\n", "20/20 [==============================] - 0s 22ms/step - loss: 0.6720 - val_loss: 0.6531\n", "Epoch 31/120\n", "20/20 [==============================] - 0s 23ms/step - loss: 0.6336 - val_loss: 0.6155\n", "Epoch 32/120\n", "20/20 [==============================] - 1s 26ms/step - loss: 0.5931 - val_loss: 0.5738\n", "Epoch 33/120\n", "20/20 [==============================] - 0s 26ms/step - loss: 0.5517 - val_loss: 0.5324\n", "Epoch 34/120\n", "20/20 [==============================] - 0s 26ms/step - loss: 0.5135 - val_loss: 0.4943\n", "Epoch 35/120\n", "20/20 [==============================] - 0s 21ms/step - loss: 0.4724 - val_loss: 0.4602\n", "Epoch 36/120\n", "20/20 [==============================] - 0s 23ms/step - loss: 0.4326 - val_loss: 0.4126\n", "Epoch 37/120\n", "20/20 [==============================] - 0s 26ms/step - loss: 0.3947 - val_loss: 0.3758\n", "Epoch 38/120\n", "20/20 [==============================] - 0s 25ms/step - loss: 0.3558 - val_loss: 0.3350\n", "Epoch 39/120\n", "20/20 [==============================] - 0s 22ms/step - loss: 0.3154 - val_loss: 0.3031\n", "Epoch 40/120\n", "20/20 [==============================] - 0s 23ms/step - loss: 0.2771 - val_loss: 0.2592\n", "Epoch 41/120\n", "20/20 [==============================] - 0s 25ms/step - loss: 0.2459 - val_loss: 0.2370\n", "Epoch 42/120\n", "20/20 [==============================] - 1s 27ms/step - loss: 0.2267 - val_loss: 0.2210\n", "Epoch 43/120\n", "20/20 [==============================] - 0s 24ms/step - loss: 0.2050 - val_loss: 0.1947\n", "Epoch 44/120\n", "20/20 [==============================] - 0s 26ms/step - loss: 0.1840 - val_loss: 0.1728\n", "Epoch 45/120\n", "20/20 [==============================] - 0s 21ms/step - loss: 0.1628 - val_loss: 0.1533\n", "Epoch 46/120\n", "20/20 [==============================] - 0s 23ms/step - loss: 0.1430 - val_loss: 0.1322\n", "Epoch 47/120\n", "20/20 [==============================] - 0s 23ms/step - loss: 0.1230 - val_loss: 0.1147\n", "Epoch 48/120\n", "20/20 [==============================] - 1s 24ms/step - loss: 0.1026 - val_loss: 0.0940\n", "Epoch 49/120\n", "20/20 [==============================] - 0s 25ms/step - loss: 0.0830 - val_loss: 0.0750\n", "Epoch 50/120\n", "20/20 [==============================] - 0s 25ms/step - loss: 0.0639 - val_loss: 0.0529\n", "Epoch 51/120\n", "20/20 [==============================] - 0s 22ms/step - loss: 0.0436 - val_loss: 0.0352\n", "Epoch 52/120\n", "20/20 [==============================] - 0s 24ms/step - loss: 0.0241 - val_loss: 0.0162\n", "Epoch 53/120\n", "20/20 [==============================] - 0s 23ms/step - loss: 0.0092 - val_loss: 0.0084\n", "Epoch 54/120\n", "20/20 [==============================] - 0s 26ms/step - loss: 0.0067 - val_loss: 0.0074\n", "Epoch 55/120\n", "20/20 [==============================] - 0s 26ms/step - loss: 0.0080 - val_loss: 0.0071\n", "Epoch 56/120\n", "20/20 [==============================] - 0s 26ms/step - loss: 0.0070 - val_loss: 0.0063\n", "Epoch 57/120\n", "20/20 [==============================] - 0s 25ms/step - loss: 0.0062 - val_loss: 0.0076\n", "Epoch 58/120\n", "20/20 [==============================] - 0s 25ms/step - loss: 0.0056 - val_loss: 0.0048\n", "Epoch 59/120\n", "20/20 [==============================] - 0s 25ms/step - loss: 0.0050 - val_loss: 0.0071\n", "Epoch 60/120\n", "20/20 [==============================] - 0s 25ms/step - loss: 0.0057 - val_loss: 0.0054\n", "Epoch 61/120\n", "20/20 [==============================] - 0s 23ms/step - loss: 0.0044 - val_loss: 0.0092\n", "Epoch 62/120\n", "20/20 [==============================] - 1s 26ms/step - loss: 0.0068 - val_loss: 0.0070\n", "Epoch 63/120\n", "20/20 [==============================] - 1s 24ms/step - loss: 0.0059 - val_loss: 0.0065\n", "Epoch 64/120\n", "20/20 [==============================] - 0s 23ms/step - loss: 0.0055 - val_loss: 0.0060\n", "Epoch 65/120\n", "20/20 [==============================] - 0s 26ms/step - loss: 0.0053 - val_loss: 0.0056\n", "Epoch 66/120\n", "20/20 [==============================] - 0s 25ms/step - loss: 0.0058 - val_loss: 0.0077\n", "Epoch 67/120\n", "20/20 [==============================] - 0s 26ms/step - loss: 0.0051 - val_loss: 0.0054\n", "Epoch 68/120\n", "20/20 [==============================] - 0s 21ms/step - loss: 0.0047 - val_loss: 0.0048\n", "Epoch 69/120\n", "20/20 [==============================] - 0s 23ms/step - loss: 0.0041 - val_loss: 0.0048\n", "Epoch 70/120\n", "20/20 [==============================] - 0s 26ms/step - loss: 0.0037 - val_loss: 0.0049\n", "Epoch 71/120\n", "20/20 [==============================] - 0s 25ms/step - loss: 0.0041 - val_loss: 0.0049\n", "Epoch 72/120\n", "20/20 [==============================] - 0s 21ms/step - loss: 0.0036 - val_loss: 0.0049\n", "Epoch 73/120\n", "20/20 [==============================] - 1s 24ms/step - loss: 0.0038 - val_loss: 0.0048\n", "Epoch 74/120\n", "20/20 [==============================] - 0s 23ms/step - loss: 0.0037 - val_loss: 0.0050\n", "Epoch 75/120\n", "20/20 [==============================] - 0s 26ms/step - loss: 0.0037 - val_loss: 0.0048\n", "Epoch 76/120\n", "20/20 [==============================] - 0s 26ms/step - loss: 0.0038 - val_loss: 0.0048\n", "Epoch 77/120\n", "20/20 [==============================] - 0s 21ms/step - loss: 0.0037 - val_loss: 0.0048\n", "Epoch 78/120\n", "20/20 [==============================] - 0s 23ms/step - loss: 0.0038 - val_loss: 0.0048\n", "Epoch 79/120\n", "20/20 [==============================] - 0s 26ms/step - loss: 0.0036 - val_loss: 0.0048\n", "Epoch 80/120\n", "20/20 [==============================] - 0s 22ms/step - loss: 0.0034 - val_loss: 0.0048\n", "Epoch 81/120\n", "20/20 [==============================] - 0s 23ms/step - loss: 0.0034 - val_loss: 0.0047\n", "Epoch 82/120\n", "20/20 [==============================] - 0s 23ms/step - loss: 0.0037 - val_loss: 0.0047\n", "Epoch 83/120\n", "20/20 [==============================] - 0s 25ms/step - loss: 0.0033 - val_loss: 0.0047\n", "Epoch 84/120\n", "20/20 [==============================] - 0s 22ms/step - loss: 0.0037 - val_loss: 0.0047\n", "Epoch 85/120\n", "20/20 [==============================] - 0s 24ms/step - loss: 0.0036 - val_loss: 0.0047\n", "Epoch 86/120\n", "20/20 [==============================] - 0s 23ms/step - loss: 0.0034 - val_loss: 0.0047\n", "Epoch 87/120\n", "20/20 [==============================] - 0s 25ms/step - loss: 0.0034 - val_loss: 0.0047\n", "Epoch 88/120\n", "20/20 [==============================] - 0s 26ms/step - loss: 0.0034 - val_loss: 0.0047\n", "Epoch 89/120\n", "20/20 [==============================] - 0s 21ms/step - loss: 0.0036 - val_loss: 0.0047\n", "Epoch 90/120\n", "20/20 [==============================] - 0s 23ms/step - loss: 0.0036 - val_loss: 0.0047\n", "Epoch 91/120\n", "20/20 [==============================] - 0s 23ms/step - loss: 0.0036 - val_loss: 0.0047\n", "Epoch 92/120\n", "20/20 [==============================] - 0s 26ms/step - loss: 0.0034 - val_loss: 0.0047\n", "Epoch 93/120\n", "20/20 [==============================] - 0s 25ms/step - loss: 0.0034 - val_loss: 0.0047\n", "Epoch 94/120\n", "20/20 [==============================] - 0s 26ms/step - loss: 0.0037 - val_loss: 0.0047\n", "Epoch 95/120\n", "20/20 [==============================] - 0s 21ms/step - loss: 0.0035 - val_loss: 0.0047\n", "Epoch 96/120\n", "20/20 [==============================] - 0s 23ms/step - loss: 0.0032 - val_loss: 0.0047\n", "Epoch 97/120\n", "20/20 [==============================] - 0s 25ms/step - loss: 0.0035 - val_loss: 0.0047\n", "Epoch 98/120\n", "20/20 [==============================] - 0s 21ms/step - loss: 0.0038 - val_loss: 0.0047\n", "Epoch 99/120\n", "20/20 [==============================] - 0s 23ms/step - loss: 0.0033 - val_loss: 0.0047\n", "Epoch 100/120\n", "20/20 [==============================] - 0s 23ms/step - loss: 0.0036 - val_loss: 0.0047\n", "Epoch 101/120\n", "20/20 [==============================] - 1s 26ms/step - loss: 0.0033 - val_loss: 0.0047\n", "Epoch 102/120\n", "20/20 [==============================] - 0s 25ms/step - loss: 0.0035 - val_loss: 0.0047\n", "Epoch 103/120\n", "20/20 [==============================] - 0s 26ms/step - loss: 0.0035 - val_loss: 0.0047\n", "Epoch 104/120\n", "20/20 [==============================] - 0s 22ms/step - loss: 0.0035 - val_loss: 0.0047\n", "Epoch 105/120\n", "20/20 [==============================] - 0s 23ms/step - loss: 0.0036 - val_loss: 0.0047\n", "Epoch 106/120\n", "20/20 [==============================] - 0s 26ms/step - loss: 0.0034 - val_loss: 0.0047\n", "Epoch 107/120\n", "20/20 [==============================] - 0s 25ms/step - loss: 0.0036 - val_loss: 0.0047\n", "Epoch 108/120\n", "20/20 [==============================] - 0s 21ms/step - loss: 0.0034 - val_loss: 0.0047\n", "Epoch 109/120\n", "20/20 [==============================] - 1s 24ms/step - loss: 0.0037 - val_loss: 0.0047\n", "Epoch 110/120\n", "20/20 [==============================] - 0s 24ms/step - loss: 0.0038 - val_loss: 0.0047\n", "Epoch 111/120\n", "20/20 [==============================] - 0s 26ms/step - loss: 0.0036 - val_loss: 0.0047\n", "Epoch 112/120\n", "20/20 [==============================] - 0s 26ms/step - loss: 0.0035 - val_loss: 0.0047\n", "Epoch 113/120\n", "20/20 [==============================] - 0s 26ms/step - loss: 0.0037 - val_loss: 0.0047\n", "Epoch 114/120\n", "20/20 [==============================] - 0s 20ms/step - loss: 0.0035 - val_loss: 0.0047\n", "Epoch 115/120\n", "20/20 [==============================] - 0s 26ms/step - loss: 0.0033 - val_loss: 0.0047\n", "Epoch 116/120\n", "20/20 [==============================] - 0s 21ms/step - loss: 0.0036 - val_loss: 0.0047\n", "Epoch 117/120\n", "20/20 [==============================] - 0s 25ms/step - loss: 0.0032 - val_loss: 0.0047\n", "Epoch 118/120\n", "20/20 [==============================] - 0s 26ms/step - loss: 0.0036 - val_loss: 0.0047\n", "Epoch 119/120\n", "20/20 [==============================] - 0s 25ms/step - loss: 0.0037 - val_loss: 0.0047\n", "Epoch 120/120\n", "20/20 [==============================] - 0s 21ms/step - loss: 0.0036 - val_loss: 0.0047\n" ] } ], "source": [ "trainable_model.compile(optimizer='adam', loss=None)\n", "hist = trainable_model.fit([X, Y[0], Y[1]], epochs=120, batch_size=8, verbose=1, \n", " validation_data=[np.expand_dims(valid[feature_cols].values, axis=1), Y_valid[0], Y_valid[1]],\n", " callbacks=[reduce_lr]\n", " )" ] }, { "cell_type": "code", "execution_count": 41, "id": "67bfbe88-5f2c-4659-b2dc-eb9f1b824d04", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[array([[0.73740077],\n", " [0.89292204],\n", " [0.7599046 ],\n", " [0.67802393],\n", " [0.6815233 ],\n", " [0.88627005],\n", " [0.6121343 ],\n", " [0.7072234 ],\n", " [0.8561135 ],\n", " [0.52762157],\n", " [0.8325021 ],\n", " [0.50241977],\n", " [0.8242289 ],\n", " [0.68957335],\n", " [0.6980361 ],\n", " [0.82116604],\n", " [0.8566438 ],\n", " [0.53687835],\n", " [0.56832707],\n", " [0.78476715],\n", " [0.85638577]], dtype=float32),\n", " array([[0.68600863],\n", " [0.78454906],\n", " [0.8179163 ],\n", " [0.94351083],\n", " [0.86383885],\n", " [0.69705516],\n", " [0.6913491 ],\n", " [0.80277354],\n", " [0.93557894],\n", " [0.82278305],\n", " [0.82674253],\n", " [0.93518937],\n", " [0.8094449 ],\n", " [0.9206344 ],\n", " [0.7747319 ],\n", " [0.9137207 ],\n", " [0.9491073 ],\n", " [0.93225 ],\n", " [0.6185102 ],\n", " [0.8867341 ],\n", " [0.82890105]], dtype=float32)]" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rst = prediction_model.predict(np.expand_dims(test[feature_cols], axis=1))\n", "rst" ] }, { "cell_type": "code", "execution_count": 42, "id": "7de501e9-05a2-424c-a5f4-85d43ad37592", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[0.9991559102070927, 0.9998196796918477]" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "[np.exp(K.get_value(log_var[0]))**0.5 for log_var in trainable_model.layers[-1].log_vars]" ] }, { "cell_type": "code", "execution_count": 43, "id": "b0d5d8ad-aadd-4218-b5b7-9691a2d3eeef", "metadata": {}, "outputs": [], "source": [ "pred_rst = pd.DataFrame.from_records(np.squeeze(np.asarray(rst), axis=2).T, columns=out_cols)" ] }, { "cell_type": "code", "execution_count": 44, "id": "0a2bcb45-da86-471b-a61d-314e29430d6a", "metadata": {}, "outputs": [], "source": [ "real_rst = test[out_cols].copy()" ] }, { "cell_type": "code", "execution_count": 45, "id": "e124f7c0-fdd5-43b9-b649-ff7d9dd59641", "metadata": {}, "outputs": [], "source": [ "for col in out_cols:\n", " pred_rst[col] = pred_rst[col] * (maxs[col] - mins[col]) + mins[col]\n", " real_rst[col] = real_rst[col] * (maxs[col] - mins[col]) + mins[col]" ] }, { "cell_type": "code", "execution_count": 46, "id": "5c69d03b-34fd-4dbf-aec6-c15093bb22ab", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['挥发分Vad(%)', '固定炭Fcad(%)'], dtype='object')" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "real_rst.columns" ] }, { "cell_type": "code", "execution_count": 47, "id": "21739f82-d82a-4bde-8537-9504b68a96d5", "metadata": {}, "outputs": [], "source": [ "y_pred_vad = pred_rst['挥发分Vad(%)'].values.reshape(-1,)\n", "y_pred_fcad = pred_rst['固定炭Fcad(%)'].values.reshape(-1,)\n", "y_true_vad = real_rst['挥发分Vad(%)'].values.reshape(-1,)\n", "y_true_fcad = real_rst['固定炭Fcad(%)'].values.reshape(-1,)" ] }, { "cell_type": "code", "execution_count": 48, "id": "26ea6cfa-efad-443c-9dd9-844f8be42b91", "metadata": {}, "outputs": [], "source": [ "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score, mean_absolute_percentage_error" ] }, { "cell_type": "code", "execution_count": 55, "id": "28072e7c-c9d5-4ff6-940d-e94ae879afc9", "metadata": {}, "outputs": [], "source": [ "def print_eva(y_true, y_pred, tp):\n", " MSE = mean_squared_error(y_true, y_pred)\n", " RMSE = np.sqrt(MSE)\n", " MAE = mean_absolute_error(y_true, y_pred)\n", " MAPE = mean_absolute_percentage_error(y_true, y_pred)\n", " R_2 = r2_score(y_true, y_pred)\n", " print(f\"COL: {tp}, MSE: {format(MSE, '.2E')}\", end=',')\n", " print(f'RMSE: {round(RMSE, 3)}', end=',')\n", " print(f'MAPE: {round(MAPE * 100, 3)} %', end=',')\n", " print(f'MAE: {round(MAE, 3)}', end=',')\n", " print(f'R_2: {round(R_2, 3)}')\n", " return [MSE, RMSE, MAE, MAPE, R_2]" ] }, { "cell_type": "code", "execution_count": 56, "id": "4ec4caa9-7c46-4fc8-a94b-cb659e924304", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "COL: 挥发分Vad, MSE: 3.35E-01,RMSE: 0.579,MAPE: 1.639 %,MAE: 0.504,R_2: 0.87\n", "COL: 固定炭Fcad, MSE: 1.11E+00,RMSE: 1.055,MAPE: 1.497 %,MAE: 0.814,R_2: 0.876\n" ] } ], "source": [ "pm25_eva = print_eva(y_true_vad, y_pred_vad, tp='挥发分Vad')\n", "pm10_eva = print_eva(y_true_fcad, y_pred_fcad, tp='固定炭Fcad')" ] }, { "cell_type": "code", "execution_count": null, "id": "ac4a4339-ec7d-4266-8197-5276c2395288", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "f15cbb91-1ce7-4fb0-979a-a4bdc452a1ec", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.16" } }, "nbformat": 4, "nbformat_minor": 5 }