coal_materials/.ipynb_checkpoints/multi-task0102-checkpoint.ipynb

In [1]:

import os
os.environ['CUDA_DEVICE_ORDER'] = 'PCB_BUS_ID'
os.environ['CUDA_VISIBLE_DEVICES'] = '0, 1'

In [2]:

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
#新增加的两行
from pylab import mpl
# 设置显示中文字体
mpl.rcParams["font.sans-serif"] = ["SimHei"]

mpl.rcParams["axes.unicode_minus"] = False

In [3]:

data_0102 = pd.read_excel('./data/20240102/20240102.xlsx', header=[0,1,2])
data_0102

Out[3]:

	Unnamed: 0_level_0	氢	碳	氮	氧	弹筒发热量	挥发分	固定炭
	化验编号	Had	Cad	Nad	Oad	Qb,ad	Vad	Fcad
	Unnamed: 0_level_2	(%)	(%)	(%)	(%)	MJ/kg	(%)	(%)
0	2720110529	3.93	70.18	0.81	25.079	27.820	32.06	55.68
1	2720096883	3.78	68.93	0.77	26.512	27.404	29.96	54.71
2	2720109084	3.48	69.60	0.76	26.148	27.578	29.31	55.99
3	2720084708	3.47	66.71	0.76	29.055	26.338	28.58	53.87
4	2720062721	3.87	68.78	0.80	26.542	27.280	29.97	54.78
...	...	...	...	...	...	...	...	...
223	2720030490	4.12	68.85	0.97	26.055	27.864	32.94	51.89
224	2720028633	3.97	67.04	0.94	28.043	27.368	31.88	51.38
225	2720028634	4.12	68.42	0.96	26.493	27.886	33.16	52.00
226	2720017683	3.88	67.42	0.94	27.760	26.616	31.65	50.56
227	2720017678	3.81	66.74	0.92	28.530	26.688	31.02	50.82

228 rows × 8 columns

In [4]:

cols = [''.join([y for y in x if 'Unnamed' not in y]) for x in data_0102.columns]
cols

Out[4]:

['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(%)']

In [5]:

data_0102.columns = cols

In [6]:

data_0102

Out[6]:

	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(%)
0	2720110529	3.93	70.18	0.81	25.079	27.820	32.06	55.68
1	2720096883	3.78	68.93	0.77	26.512	27.404	29.96	54.71
2	2720109084	3.48	69.60	0.76	26.148	27.578	29.31	55.99
3	2720084708	3.47	66.71	0.76	29.055	26.338	28.58	53.87
4	2720062721	3.87	68.78	0.80	26.542	27.280	29.97	54.78
...	...	...	...	...	...	...	...	...
223	2720030490	4.12	68.85	0.97	26.055	27.864	32.94	51.89
224	2720028633	3.97	67.04	0.94	28.043	27.368	31.88	51.38
225	2720028634	4.12	68.42	0.96	26.493	27.886	33.16	52.00
226	2720017683	3.88	67.42	0.94	27.760	26.616	31.65	50.56
227	2720017678	3.81	66.74	0.92	28.530	26.688	31.02	50.82

228 rows × 8 columns

In [7]:

out_cols = ['挥发分Vad(%)', '固定炭Fcad(%)']

In [8]:

out_cols

Out[8]:

['挥发分Vad(%)', '固定炭Fcad(%)']

In [9]:

data = data_0102.copy()

In [10]:

train_data = data.dropna(subset=out_cols).fillna(0)

In [11]:

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import tensorflow.keras.backend as K

2024-01-05 16:46:07.061819: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0

In [12]:

class TransformerBlock(layers.Layer):
    def __init__(self, embed_dim, num_heads, ff_dim, name, rate=0.1):
        super().__init__()
        self.att = layers.MultiHeadAttention(num_heads=num_heads, key_dim=embed_dim, name=name)
        self.ffn = keras.Sequential(
            [layers.Dense(ff_dim, activation="relu"), layers.Dense(embed_dim),]
        )
        self.layernorm1 = layers.LayerNormalization(epsilon=1e-6)
        self.layernorm2 = layers.LayerNormalization(epsilon=1e-6)
        self.dropout1 = layers.Dropout(rate)
        self.dropout2 = layers.Dropout(rate)

    def call(self, inputs, training):
        attn_output = self.att(inputs, inputs)
        attn_output = self.dropout1(attn_output, training=training)
        out1 = self.layernorm1(inputs + attn_output)
        ffn_output = self.ffn(out1)
        ffn_output = self.dropout2(ffn_output, training=training)
        return self.layernorm2(out1 + ffn_output)

In [13]:

from tensorflow.keras import Model

In [14]:

from tensorflow.keras.initializers import Constant

In [15]:

# Custom loss layer
class CustomMultiLossLayer(layers.Layer):
    def __init__(self, nb_outputs=2, **kwargs):
        self.nb_outputs = nb_outputs
        self.is_placeholder = True
        super(CustomMultiLossLayer, self).__init__(**kwargs)
        
    def build(self, input_shape=None):
        # initialise log_vars
        self.log_vars = []
        for i in range(self.nb_outputs):
            self.log_vars += [self.add_weight(name='log_var' + str(i), shape=(1,),
                                              initializer=tf.initializers.he_normal(), trainable=True)]
        super(CustomMultiLossLayer, self).build(input_shape)

    def multi_loss(self, ys_true, ys_pred):
        assert len(ys_true) == self.nb_outputs and len(ys_pred) == self.nb_outputs
        loss = 0
        for y_true, y_pred, log_var in zip(ys_true, ys_pred, self.log_vars):
            mse = (y_true - y_pred) ** 2.
            pre = K.exp(-log_var[0])
            loss += tf.abs(tf.reduce_logsumexp(pre * mse + log_var[0], axis=-1))
        return K.mean(loss)

    def call(self, inputs):
        ys_true = inputs[:self.nb_outputs]
        ys_pred = inputs[self.nb_outputs:]
        loss = self.multi_loss(ys_true, ys_pred)
        self.add_loss(loss, inputs=inputs)
        # We won't actually use the output.
        return K.concatenate(inputs, -1)

In [16]:

num_heads, ff_dim = 3, 16

In [17]:

def get_prediction_model():
    inputs = layers.Input(shape=(1,len(feature_cols)), name='input')
    x = layers.Conv1D(filters=64, kernel_size=1, activation='relu')(inputs)
    # x = layers.Dropout(rate=0.1)(x)
    lstm_out = layers.Bidirectional(layers.LSTM(units=64, return_sequences=True))(x)
    lstm_out = layers.Dense(128, activation='relu')(lstm_out)
    transformer_block = TransformerBlock(128, num_heads, ff_dim, name='first_attn')
    out = transformer_block(lstm_out)
    out = layers.GlobalAveragePooling1D()(out)
    out = layers.Dropout(0.1)(out)
    out = layers.Dense(64, activation='relu')(out)
    # out = K.expand_dims(out, axis=1)

    bet = layers.Dense(32, activation="relu")(out)
    mesco = layers.Dense(32, activation="relu")(out)

    bet = layers.Dense(1, activation='sigmoid', name='vad')(bet)
    mesco = layers.Dense(1, activation='sigmoid', name='fcad')(mesco)

    model = Model(inputs=[inputs], outputs=[bet, mesco])
    return model

In [18]:

def get_trainable_model(prediction_model):
    inputs = layers.Input(shape=(1,len(feature_cols)), name='input')
    bet, mesco = prediction_model(inputs)
    bet_real = layers.Input(shape=(1,), name='vad_real')
    mesco_real = layers.Input(shape=(1,), name='fcad_real')
    out = CustomMultiLossLayer(nb_outputs=2)([bet_real, mesco_real, bet, mesco])
    return Model([inputs, bet_real, mesco_real], out)

In [19]:

from tensorflow.python.keras.utils.vis_utils import plot_model

In [20]:

maxs = train_data.max()
mins = train_data.min()
for col in train_data.columns:
    if maxs[col] - mins[col] == 0:
        continue
    train_data[col] = (train_data[col] - mins[col]) / (maxs[col] - mins[col])

In [21]:

train_data

Out[21]:

	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(%)
0	0.996547	0.773973	0.835414	0.456522	0.171463	0.811249	0.847737	0.828147
1	0.851118	0.671233	0.799943	0.369565	0.210254	0.782038	0.674897	0.794606
2	0.981147	0.465753	0.818956	0.347826	0.200401	0.794256	0.621399	0.838866
3	0.721367	0.458904	0.736947	0.347826	0.279094	0.707183	0.561317	0.765560
4	0.487046	0.732877	0.795687	0.434783	0.211066	0.773331	0.675720	0.797026
...	...	...	...	...	...	...	...	...
223	0.143553	0.904110	0.797673	0.804348	0.197883	0.814339	0.920165	0.697095
224	0.123762	0.801370	0.746311	0.739130	0.251699	0.779510	0.832922	0.679461
225	0.123773	0.904110	0.785471	0.782609	0.209740	0.815884	0.938272	0.700899
226	0.007066	0.739726	0.757094	0.739130	0.244038	0.726705	0.813992	0.651107
227	0.007012	0.691781	0.737798	0.695652	0.264882	0.731760	0.762140	0.660097

228 rows × 8 columns

In [22]:

# feature_cols = [x for x in train_data.columns if x not in out_cols and '第二次' not in x]
feature_cols = [x for x in train_data.columns if x not in out_cols]
use_cols = feature_cols + out_cols

In [23]:

use_data = train_data.copy()
for col in use_cols:
    use_data[col] = use_data[col].astype('float32')

In [24]:

from sklearn.model_selection import KFold, train_test_split
kf = KFold(n_splits=6, shuffle=True, random_state=42)

In [25]:

from tensorflow.keras import optimizers
from tensorflow.python.keras.utils.vis_utils import plot_model
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score, mean_absolute_percentage_error

In [26]:

from keras.callbacks import ReduceLROnPlateau
reduce_lr = ReduceLROnPlateau(monitor='val_loss', patience=10, mode='auto')

In [27]:

def print_eva(y_true, y_pred, tp):
    MSE = mean_squared_error(y_true, y_pred)
    RMSE = np.sqrt(MSE)
    MAE = mean_absolute_error(y_true, y_pred)
    MAPE = mean_absolute_percentage_error(y_true, y_pred)
    R_2 = r2_score(y_true, y_pred)
    print(f"COL: {tp}, MSE: {format(MSE, '.2E')}", end=',')
    print(f'RMSE: {round(RMSE, 3)}', end=',')
    print(f'MAPE: {round(MAPE * 100, 3)} %', end=',')
    print(f'MAE: {round(MAE, 3)}', end=',')
    print(f'R_2: {round(R_2, 3)}')
    return [MSE, RMSE, MAE, MAPE, R_2]

In [28]:

prediction_model = get_prediction_model()
trainable_model = get_trainable_model(prediction_model)
trainable_model.compile(optimizer='adam', loss=None)

2024-01-05 16:46:22.503307: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcuda.so.1
2024-01-05 16:46:22.560854: E tensorflow/stream_executor/cuda/cuda_driver.cc:328] failed call to cuInit: CUDA_ERROR_INVALID_DEVICE: invalid device ordinal
2024-01-05 16:46:22.560909: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:169] retrieving CUDA diagnostic information for host: zhaojh-yv621
2024-01-05 16:46:22.560920: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:176] hostname: zhaojh-yv621
2024-01-05 16:46:22.561113: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:200] libcuda reported version is: 520.61.5
2024-01-05 16:46:22.561132: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:204] kernel reported version is: 520.61.5
2024-01-05 16:46:22.561135: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:310] kernel version seems to match DSO: 520.61.5
2024-01-05 16:46:22.561424: 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
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[28], line 2
      1 prediction_model = get_prediction_model()
----> 2 trainable_model = get_trainable_model(prediction_model)
      3 trainable_model.compile(optimizer='adam', loss=None)

Cell In[18], line 6, in get_trainable_model(prediction_model)
      4 bet_real = layers.Input(shape=(1,), name='vad_real')
      5 mesco_real = layers.Input(shape=(1,), name='fcad_real')
----> 6 out = CustomMultiLossLayer(nb_outputs=2)([bet_real, mesco_real, bet, mesco])
      7 return Model([inputs, bet_real, mesco_real], out)

File ~/miniconda3/envs/python38/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py:969, in Layer.__call__(self, *args, **kwargs)
    963 # Functional Model construction mode is invoked when `Layer`s are called on
    964 # symbolic `KerasTensor`s, i.e.:
    965 # >> inputs = tf.keras.Input(10)
    966 # >> outputs = MyLayer()(inputs)  # Functional construction mode.
    967 # >> model = tf.keras.Model(inputs, outputs)
    968 if _in_functional_construction_mode(self, inputs, args, kwargs, input_list):
--> 969   return self._functional_construction_call(inputs, args, kwargs,
    970                                             input_list)
    972 # Maintains info about the `Layer.call` stack.
    973 call_context = base_layer_utils.call_context()

File ~/miniconda3/envs/python38/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py:1107, in Layer._functional_construction_call(self, inputs, args, kwargs, input_list)
   1102     training_arg_passed_by_framework = True
   1104 with call_context.enter(
   1105     layer=self, inputs=inputs, build_graph=True, training=training_value):
   1106   # Check input assumptions set after layer building, e.g. input shape.
-> 1107   outputs = self._keras_tensor_symbolic_call(
   1108       inputs, input_masks, args, kwargs)
   1110   if outputs is None:
   1111     raise ValueError('A layer\'s `call` method should return a '
   1112                      'Tensor or a list of Tensors, not None '
   1113                      '(layer: ' + self.name + ').')

File ~/miniconda3/envs/python38/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py:840, in Layer._keras_tensor_symbolic_call(self, inputs, input_masks, args, kwargs)
    838   return nest.map_structure(keras_tensor.KerasTensor, output_signature)
    839 else:
--> 840   return self._infer_output_signature(inputs, args, kwargs, input_masks)

File ~/miniconda3/envs/python38/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py:880, in Layer._infer_output_signature(self, inputs, args, kwargs, input_masks)
    878     self._maybe_build(inputs)
    879     inputs = self._maybe_cast_inputs(inputs)
--> 880     outputs = call_fn(inputs, *args, **kwargs)
    882   self._handle_activity_regularization(inputs, outputs)
    883 self._set_mask_metadata(inputs, outputs, input_masks,
    884                         build_graph=False)

File ~/miniconda3/envs/python38/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py:695, in convert.<locals>.decorator.<locals>.wrapper(*args, **kwargs)
    693 except Exception as e:  # pylint:disable=broad-except
    694   if hasattr(e, 'ag_error_metadata'):
--> 695     raise e.ag_error_metadata.to_exception(e)
    696   else:
    697     raise

ValueError: in user code:

    /tmp/ipykernel_16320/2404117700.py:31 call  *
        return K.concatenate(inputs, -1)
    /root/miniconda3/envs/python38/lib/python3.8/site-packages/tensorflow/python/util/dispatch.py:206 wrapper  **
        return target(*args, **kwargs)
    /root/miniconda3/envs/python38/lib/python3.8/site-packages/tensorflow/python/keras/backend.py:3098 concatenate
        return array_ops.concat([to_dense(x) for x in tensors], axis)
    /root/miniconda3/envs/python38/lib/python3.8/site-packages/tensorflow/python/util/dispatch.py:206 wrapper
        return target(*args, **kwargs)
    /root/miniconda3/envs/python38/lib/python3.8/site-packages/tensorflow/python/ops/array_ops.py:1768 concat
        return gen_array_ops.concat_v2(values=values, axis=axis, name=name)
    /root/miniconda3/envs/python38/lib/python3.8/site-packages/tensorflow/python/ops/gen_array_ops.py:1227 concat_v2
        _, _, _op, _outputs = _op_def_library._apply_op_helper(
    /root/miniconda3/envs/python38/lib/python3.8/site-packages/tensorflow/python/framework/op_def_library.py:748 _apply_op_helper
        op = g._create_op_internal(op_type_name, inputs, dtypes=None,
    /root/miniconda3/envs/python38/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py:599 _create_op_internal
        return super(FuncGraph, self)._create_op_internal(  # pylint: disable=protected-access
    /root/miniconda3/envs/python38/lib/python3.8/site-packages/tensorflow/python/framework/ops.py:3557 _create_op_internal
        ret = Operation(
    /root/miniconda3/envs/python38/lib/python3.8/site-packages/tensorflow/python/framework/ops.py:2041 __init__
        self._c_op = _create_c_op(self._graph, node_def, inputs,
    /root/miniconda3/envs/python38/lib/python3.8/site-packages/tensorflow/python/framework/ops.py:1883 _create_c_op
        raise ValueError(str(e))

    ValueError: Shape must be rank 2 but is rank 3 for '{{node custom_multi_loss_layer/concat}} = ConcatV2[N=4, T=DT_FLOAT, Tidx=DT_INT32](Placeholder, Placeholder_1, Placeholder_2, Placeholder_3, custom_multi_loss_layer/concat/axis)' with input shapes: [?,1], [?,1], [?,1,1], [?,1,1], [].

In [ ]:

plot_model(prediction_model)

In [28]:

vad_eva_list = list()
fcad_eva_list = list()
train_data = use_data[use_cols].copy()
for (train_index, test_index) in kf.split(train_data):
    train = train_data.loc[train_index]
    valid = train_data.loc[test_index]
    X = np.expand_dims(train[feature_cols].values, axis=1)
    Y = [x for x in train[out_cols].values.T]
    X_valid = np.expand_dims(valid[feature_cols].values, axis=1)
    Y_valid = [x for x in valid[out_cols].values.T]
    prediction_model = get_prediction_model()
    trainable_model = get_trainable_model(prediction_model)
    trainable_model.compile(optimizer='adam', loss=None)
    hist = trainable_model.fit([X, Y[0], Y[1]], epochs=120, batch_size=8, verbose=0, 
                               validation_data=[X_valid, Y_valid[0], Y_valid[1]],
                               callbacks=[reduce_lr]
                               )
    rst = prediction_model.predict(X_valid)
    pred_rst = pd.DataFrame.from_records(np.squeeze(np.asarray(rst), axis=2).T, columns=out_cols)
    real_rst = valid[out_cols].copy()
    for col in out_cols:
        pred_rst[col] = pred_rst[col] * (maxs[col] - mins[col]) + mins[col]
        real_rst[col] = real_rst[col] * (maxs[col] - mins[col]) + mins[col]
    y_pred_vad = pred_rst['挥发分Vad(%)'].values.reshape(-1,)
    y_pred_fcad = pred_rst['固定炭Fcad(%)'].values.reshape(-1,)
    y_true_vad = real_rst['挥发分Vad(%)'].values.reshape(-1,)
    y_true_fcad = real_rst['固定炭Fcad(%)'].values.reshape(-1,)
    vad_eva = print_eva(y_true_vad, y_pred_vad, tp='挥发分Vad')
    fcad_eva = print_eva(y_true_fcad, y_pred_fcad, tp='固定炭Fcad')
    vad_eva_list.append(vad_eva)
    fcad_eva_list.append(fcad_eva)
    del trainable_model
    del prediction_model

2024-01-05 13:55:16.952556: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:176] None of the MLIR Optimization Passes are enabled (registered 2)
2024-01-05 13:55:16.970806: I tensorflow/core/platform/profile_utils/cpu_utils.cc:114] CPU Frequency: 2200000000 Hz

COL: 挥发分Vad, MSE: 5.39E-01,RMSE: 0.734,MAPE: 1.948 %,MAE: 0.582,R_2: 0.883
COL: 固定炭Fcad, MSE: 7.77E-01,RMSE: 0.881,MAPE: 1.246 %,MAE: 0.654,R_2: 0.969
COL: 挥发分Vad, MSE: 8.80E-01,RMSE: 0.938,MAPE: 2.679 %,MAE: 0.783,R_2: 0.893
COL: 固定炭Fcad, MSE: 1.32E+00,RMSE: 1.149,MAPE: 1.814 %,MAE: 0.907,R_2: 0.974
COL: 挥发分Vad, MSE: 6.68E-01,RMSE: 0.817,MAPE: 2.064 %,MAE: 0.606,R_2: 0.829
COL: 固定炭Fcad, MSE: 9.89E-01,RMSE: 0.995,MAPE: 1.427 %,MAE: 0.798,R_2: 0.929
COL: 挥发分Vad, MSE: 6.34E-01,RMSE: 0.796,MAPE: 2.099 %,MAE: 0.62,R_2: 0.889
COL: 固定炭Fcad, MSE: 4.93E-01,RMSE: 0.702,MAPE: 1.058 %,MAE: 0.542,R_2: 0.985
WARNING:tensorflow:5 out of the last 9 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7f0801c91c10> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
COL: 挥发分Vad, MSE: 2.34E+00,RMSE: 1.53,MAPE: 4.385 %,MAE: 1.317,R_2: 0.467
COL: 固定炭Fcad, MSE: 2.21E+02,RMSE: 14.87,MAPE: 27.662 %,MAE: 14.835,R_2: -9.385
WARNING:tensorflow:6 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7f0801cf34c0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
COL: 挥发分Vad, MSE: 6.16E-01,RMSE: 0.785,MAPE: 2.29 %,MAE: 0.674,R_2: 0.873
COL: 固定炭Fcad, MSE: 1.04E+00,RMSE: 1.02,MAPE: 1.603 %,MAE: 0.811,R_2: 0.956

In [33]:

vad_df = pd.DataFrame.from_records(vad_eva_list, columns=['MSE', 'RMSE', 'MAE', 'MAPE', 'R_2'])
vad_df.sort_values(by='R_2')[1:].mean()

Out[33]:

MSE     0.667414
RMSE    0.814141
MAE     0.652951
MAPE    0.022159
R_2     0.873633
dtype: float64

In [34]:

fcad_df = pd.DataFrame.from_records(fcad_eva_list, columns=['MSE', 'RMSE', 'MAE', 'MAPE', 'R_2'])
fcad_df.sort_values(by='R_2')[1:].mean()

Out[34]:

MSE     0.923848
RMSE    0.949375
MAE     0.742411
MAPE    0.014295
R_2     0.962834
dtype: float64

In [ ]:

train, valid = train_test_split(use_data[use_cols], test_size=0.3, random_state=42, shuffle=True)
valid, test = train_test_split(valid, test_size=0.3, random_state=42, shuffle=True)

In [31]:

prediction_model = get_prediction_model()
trainable_model = get_trainable_model(prediction_model)

In [34]:

X = np.expand_dims(train[feature_cols].values, axis=1)
Y = [x for x in train[out_cols].values.T]
Y_valid = [x for x in valid[out_cols].values.T]

In [ ]:

trainable_model.compile(optimizer='adam', loss=None)
hist = trainable_model.fit([X, Y[0], Y[1]], epochs=120, batch_size=8, verbose=1, 
                           validation_data=[np.expand_dims(valid[feature_cols].values, axis=1), Y_valid[0], Y_valid[1]],
                           callbacks=[reduce_lr]
                           )

In [41]:

rst = prediction_model.predict(np.expand_dims(test[feature_cols], axis=1))
rst

Out[41]:

[array([[0.73740077],
        [0.89292204],
        [0.7599046 ],
        [0.67802393],
        [0.6815233 ],
        [0.88627005],
        [0.6121343 ],
        [0.7072234 ],
        [0.8561135 ],
        [0.52762157],
        [0.8325021 ],
        [0.50241977],
        [0.8242289 ],
        [0.68957335],
        [0.6980361 ],
        [0.82116604],
        [0.8566438 ],
        [0.53687835],
        [0.56832707],
        [0.78476715],
        [0.85638577]], dtype=float32),
 array([[0.68600863],
        [0.78454906],
        [0.8179163 ],
        [0.94351083],
        [0.86383885],
        [0.69705516],
        [0.6913491 ],
        [0.80277354],
        [0.93557894],
        [0.82278305],
        [0.82674253],
        [0.93518937],
        [0.8094449 ],
        [0.9206344 ],
        [0.7747319 ],
        [0.9137207 ],
        [0.9491073 ],
        [0.93225   ],
        [0.6185102 ],
        [0.8867341 ],
        [0.82890105]], dtype=float32)]

In [42]:

[np.exp(K.get_value(log_var[0]))**0.5 for log_var in trainable_model.layers[-1].log_vars]

Out[42]:

[0.9991559102070927, 0.9998196796918477]

In [46]:

real_rst.columns

Out[46]:

Index(['挥发分Vad(%)', '固定炭Fcad(%)'], dtype='object')

In [ ]:

for col in out_cols:
    pred_rst[col] = pred_rst[col] * (maxs[col] - mins[col]) + mins[col]
    real_rst[col] = real_rst[col] * (maxs[col] - mins[col]) + mins[col]

In [47]:

y_pred_vad = pred_rst['挥发分Vad(%)'].values.reshape(-1,)
y_pred_fcad = pred_rst['固定炭Fcad(%)'].values.reshape(-1,)
y_true_vad = real_rst['挥发分Vad(%)'].values.reshape(-1,)
y_true_fcad = real_rst['固定炭Fcad(%)'].values.reshape(-1,)

In [56]:

pm25_eva = print_eva(y_true_vad, y_pred_vad, tp='挥发分Vad')
pm10_eva = print_eva(y_true_fcad, y_pred_fcad, tp='固定炭Fcad')

COL: 挥发分Vad, MSE: 3.35E-01,RMSE: 0.579,MAPE: 1.639 %,MAE: 0.504,R_2: 0.87
COL: 固定炭Fcad, MSE: 1.11E+00,RMSE: 1.055,MAPE: 1.497 %,MAE: 0.814,R_2: 0.876

In [ ]:

In [ ]: