Commit d95eddc8 authored by ukuiq's avatar ukuiq
Browse files

added LSTM models

parent 9b2a20c5
%% Cell type:code id: tags:
``` python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
import os
from scipy.signal import lfilter
```
%% Cell type:code id: tags:
``` python
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error
import numpy
from keras.datasets import imdb
from keras.layers.embeddings import Embedding
from keras.preprocessing import sequence
from tensorflow.keras import activations
from keras.layers import Activation, Dense
from keras.preprocessing.sequence import TimeseriesGenerator
from sklearn.model_selection import train_test_split
import tensorflow as tf
from keras.layers import Dense, Softmax, Dropout, BatchNormalization
```
%% Cell type:code id: tags:
``` python
path_0 = '../data/preprocessed/clusters/0/'
path_1 = '../data/preprocessed/clusters/1/'
path_2 = '../data/preprocessed/clusters/2/'
dfs_c_0 = []
dfs_c_1 = []
dfs_c_2 = []
dfs_c_0_grouped = []
dfs_c_1_grouped = []
dfs_c_2_grouped = []
csv_files_0 = [csv for csv in os.listdir(path_0) if csv.endswith('.csv')]
csv_files_1 = [csv for csv in os.listdir(path_1) if csv.endswith('.csv')]
csv_files_2 = [csv for csv in os.listdir(path_2) if csv.endswith('.csv')]
for file in csv_files_0:
# import DataFrame
df = pd.read_csv(path_0 + file)
if file.startswith('g_'):
dfs_c_0_grouped.append(df)
else:
dfs_c_0.append(df)
for file in csv_files_1:
# import DataFrame
df = pd.read_csv(path_1 + file)
if file.startswith('g_'):
dfs_c_1_grouped.append(df)
else:
dfs_c_1.append(df)
for file in csv_files_2:
# import DataFrame
df = pd.read_csv(path_2 + file)
if file.startswith('g_'):
dfs_c_2_grouped.append(df)
else:
dfs_c_2.append(df)
```
%% Cell type:code id: tags:
``` python
def create_windows_smoothed(dfs, length, batch_size, stride):
features = []
targets = []
X = []
y = []
# Height as feature and target
for df in dfs:
height = df['inter_pol'].to_numpy().tolist()
# apply TimeSeriesGenerator
ts_generator = TimeseriesGenerator(height,height,length=length, batch_size=batch_size, stride=stride)
for j in range(len(ts_generator)):
features.append(ts_generator[j][0])
targets.append(ts_generator[j][1])
#reshape data for neural network
for i in range(len(features)):
x = np.reshape(features[i], (length,1))
X.append(x)
X = np.array(X)
y = np.array(targets)
return X, y
```
%% Cell type:code id: tags:
``` python
# create lstm input
X,y = create_windows_smoothed(dfs_c_0, 40, 1, 20)
#Split data into train & test set & validation set
X_tr_2, X_val_2, y_tr_2, y_val_2 = train_test_split(X, y, test_size=0.1)
X_train_2, X_test_2, y_train_2, y_test_2 = train_test_split(X_tr_2, y_tr_2, test_size=0.2)
```
%% Cell type:code id: tags:
``` python
print(X_train_2.shape)
print(y_train_2.shape)
print(X_test_2.shape)
print(y_test_2.shape)
print(X_val_2.shape)
print(y_val_2.shape)
```
%%%% Output: stream
(3563, 40, 1)
(3563, 1)
(891, 40, 1)
(891, 1)
(495, 40, 1)
(495, 1)
%% Cell type:code id: tags:
``` python
# build lstm
model01 = Sequential()
model01.add(LSTM(200, return_sequences=True, input_shape= (40, 1)))
model01.add(LSTM(200))
model01.add(Dense(1, activation='relu'))
#optimizer = optimizers.Adam(clipvalue=0.5)
adam = tf.keras.optimizers.Adam(learning_rate=0.001) # , clipnorm=1
model01.compile(optimizer=adam, loss='mse',metrics=['mean_absolute_error'])
```
%% Cell type:code id: tags:
``` python
model01.fit(X_train_2, y_train_2, epochs=20, batch_size=64, validation_data=(X_test_2, y_test_2), verbose=1, shuffle=True)
```
%% Cell type:code id: tags:
``` python
from keras.models import load_model
model01 = load_model('c0_lstm.h5')
```
%% Cell type:code id: tags:
``` python
model01.summary()
```
%%%% Output: stream
Model: "sequential_11"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
lstm_20 (LSTM) (None, 40, 200) 161600
_________________________________________________________________
lstm_21 (LSTM) (None, 200) 320800
_________________________________________________________________
dense_11 (Dense) (None, 1) 201
=================================================================
Total params: 482,601
Trainable params: 482,601
Non-trainable params: 0
_________________________________________________________________
%% Cell type:code id: tags:
``` python
initial_set = X_val_2[0]
initial_set
```
%%%% Output: execute_result
array([[84.4 ],
[83.93333333],
[83.53333333],
[83.33333333],
[83.13333333],
[82.73333333],
[82.26666667],
[81.73333333],
[81.13333333],
[80.53333333],
[79.93333333],
[79.33333333],
[78.93333333],
[78.4 ],
[77.86666667],
[77.33333333],
[76.8 ],
[76.33333333],
[75.8 ],
[75.2 ],
[74.6 ],
[74.93333333],
[75.33333333],
[75.53333333],
[75.73333333],
[74.93333333],
[75.06666667],
[75.06666667],
[74.86666667],
[73.93333333],
[73.13333333],
[72.46666667],
[71.86666667],
[71.06666667],
[70.26666667],
[69.66666667],
[69.06666667],
[68.53333333],
[67.93333333],
[67.33333333]])
%% Cell type:code id: tags:
``` python
result2 = []
for i in range(0, 500):
x_input = initial_set.reshape((1, 40, 1))
yhat = model01.predict(x_input, verbose=0)
result2.append(yhat[0][0])
initial_set = np.append(initial_set, yhat)
initial_set = np.delete(initial_set, 0)
```
%% Cell type:code id: tags:
``` python
plt.figure(figsize=(30,8))
plt.ylim((0,200))
plt.title("LSTM trained on smoothed, grouped data")
plt.xticks(fontsize=8, rotation=90)
plt.yticks(fontsize=10, fontweight='bold')
plt.plot(result2)
plt.legend(['Predicted values'], loc='upper left')
plt.show()
```
%%%% Output: display_data
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%% Cell type:code id: tags:
``` python
initial_set2 = []
for i in range(1, 41, 1):
initial_set2.append(140)
initial_set2 = np.array(initial_set2)
initial = initial_set2
initial_set2
```
%%%% Output: execute_result
array([140, 140, 140, 140, 140, 140, 140, 140, 140, 140, 140, 140, 140,
140, 140, 140, 140, 140, 140, 140, 140, 140, 140, 140, 140, 140,
140, 140, 140, 140, 140, 140, 140, 140, 140, 140, 140, 140, 140,
140])
%% Cell type:code id: tags:
``` python
result3 = []
for i in range(0, 500):
x_input = initial_set2.reshape((1, 40, 1))
yhat = model01.predict(x_input, verbose=0)
result3.append(yhat[0][0])
initial_set2 = np.append(initial_set2, yhat)
initial_set2 = np.delete(initial_set2, 0)
gradients = np.gradient(initial_set2)
if ((sum(gradients) / len(gradients))) > -1 and (initial_set2[39] < 20):
initial_set2 = initial
```
%% Cell type:code id: tags:
``` python
plt.figure(figsize=(30,8))
plt.ylim((0,200))
plt.title("LSTM trained on smoothed, grouped data")
plt.xticks(np.arange(0, 500, step=60), fontsize=8, rotation=90, )
plt.yticks(fontsize=10, fontweight='bold')
plt.plot(result3)
plt.legend(['Predicted values'], loc='upper left')
plt.show()
```
%%%% Output: display_data
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%% Cell type:code id: tags:
``` python
dfs_c_0
```
%%%% Output: execute_result
[ Unnamed: 0 device_id time_stamp Height \
0 0 70B3D500700016E5 2020-05-09 01:49:33.615246 96
1 1 70B3D500700016E5 2020-05-09 02:49:37.365573 96
2 2 70B3D500700016E5 2020-05-09 03:49:33.130492 96
3 3 70B3D500700016E5 2020-05-09 04:49:32.877295 96
4 4 70B3D500700016E5 2020-05-09 06:49:32.415491 96
... ... ... ... ...
7228 7230 70B3D500700016E5 2021-05-08 16:14:47.805898 126
7229 7231 70B3D500700016E5 2021-05-08 17:14:47.563265 126
7230 7232 70B3D500700016E5 2021-05-08 18:14:47.288859 126
7231 7233 70B3D500700016E5 2021-05-08 19:14:48.629415 124
7232 7234 70B3D500700016E5 2021-05-08 22:14:48.112348 132
Temperature Tilt lfilter mov_avg min_avg inter_pol
0 12.0 1 1.600000 NaN NaN 89.266667
1 13.0 1 3.200000 NaN NaN 89.266667
2 11.0 2 4.800000 NaN NaN 89.266667
3 10.0 1 6.400000 NaN NaN 89.266667
4 8.0 2 8.000000 NaN NaN 89.266667
... ... ... ... ... ... ...
7228 27.0 1 108.300000 118.466667 96.0 118.466667
7229 24.0 1 108.566667 119.466667 96.0 119.466667
7230 21.0 0 108.833333 120.400000 96.0 120.400000
7231 16.0 1 109.066667 121.333333 96.0 121.333333
7232 10.0 1 109.666667 122.466667 96.0 122.466667
[7233 rows x 10 columns],
Unnamed: 0 device_id time_stamp Height \
0 0 70B3D500700016F1 2020-05-09 00:34:23.845544 84
1 1 70B3D500700016F1 2020-05-09 01:34:23.699359 84
2 2 70B3D500700016F1 2020-05-09 02:34:23.547372 84
3 3 70B3D500700016F1 2020-05-09 03:34:23.395129 84
4 4 70B3D500700016F1 2020-05-09 05:34:23.110478 84
... ... ... ... ...
7850 7851 70B3D500700016F1 2021-05-08 16:12:58.267171 106
7851 7852 70B3D500700016F1 2021-05-08 17:12:58.097596 106
7852 7853 70B3D500700016F1 2021-05-08 18:12:57.891567 104
7853 7854 70B3D500700016F1 2021-05-08 20:12:59.235848 104
7854 7855 70B3D500700016F1 2021-05-08 22:12:58.943461 102
Temperature Tilt lfilter mov_avg min_avg inter_pol
0 15.0 0 1.400000 NaN NaN 86.000000
1 15.0 1 2.800000 NaN NaN 86.000000
2 13.0 1 4.200000 NaN NaN 86.000000
3 14.0 0 5.600000 NaN NaN 86.000000
4 15.0 0 7.000000 NaN NaN 86.000000
... ... ... ... ... ... ...
7850 31.0 0 121.133333 112.466667 106.0 112.466667
7851 29.0 0 121.566667 111.933333 106.0 111.933333
7852 25.0 0 121.966667 111.333333 104.0 111.333333
7853 18.0 0 121.333333 110.733333 104.0 110.733333
7854 11.0 0 120.666667 110.066667 102.0 110.066667
[7855 rows x 10 columns],
Unnamed: 0 device_id time_stamp Height \
0 0 70B3D500700016F4 2020-05-09 00:30:46.645843 60
1 1 70B3D500700016F4 2020-05-09 01:30:46.466883 60
2 2 70B3D500700016F4 2020-05-09 02:30:46.286815 60
3 3 70B3D500700016F4 2020-05-09 03:30:46.110369 60
4 4 70B3D500700016F4 2020-05-09 04:30:45.925179 60
... ... ... ... ...
3527 3530 70B3D500700016F4 2021-05-08 18:04:20.859170 62
3528 3531 70B3D500700016F4 2021-05-08 19:04:22.253953 60
3529 3532 70B3D500700016F4 2021-05-08 20:04:22.088932 60
3530 3533 70B3D500700016F4 2021-05-08 21:04:21.967920 62
3531 3534 70B3D500700016F4 2021-05-08 22:04:21.781690 60
Temperature Tilt lfilter mov_avg min_avg inter_pol
0 15.0 0 1.000000 NaN NaN 68.533333
1 13.0 0 2.000000 NaN NaN 68.533333
2 12.0 0 3.000000 NaN NaN 68.533333
3 11.0 0 4.000000 NaN NaN 68.533333
4 11.0 0 5.000000 NaN NaN 68.533333
... ... ... ... ... ... ...
3527 23.0 0 78.366667 72.400000 60.0 72.400000
3528 18.0 0 78.533333 70.000000 60.0 70.000000
3529 17.0 0 78.766667 67.533333 60.0 67.533333
3530 14.0 0 79.033333 65.000000 60.0 65.000000
3531 9.0 0 79.233333 62.600000 60.0 62.600000
[3532 rows x 10 columns],
Unnamed: 0 device_id time_stamp Height \
0 0 70B3D500700016F6 2020-05-09 00:55:40.099451 26
1 1 70B3D500700016F6 2020-05-09 02:55:39.674995 26
2 2 70B3D500700016F6 2020-05-09 03:55:39.484016 26
3 3 70B3D500700016F6 2020-05-09 04:55:39.274142 26
4 4 70B3D500700016F6 2020-05-09 05:55:39.079391 26
... ... ... ... ...
7785 7785 70B3D500700016F6 2021-05-08 16:26:17.336651 64
7786 7786 70B3D500700016F6 2021-05-08 17:26:17.131030 122
7787 7787 70B3D500700016F6 2021-05-08 18:26:16.959249 116
7788 7788 70B3D500700016F6 2021-05-08 19:26:24.328798 116
7789 7789 70B3D500700016F6 2021-05-08 20:26:18.141285 118
Temperature Tilt lfilter mov_avg min_avg inter_pol
0 20.0 0 0.433333 NaN NaN 73.266667
1 19.0 1 0.866667 NaN NaN 73.266667
2 16.0 1 1.300000 NaN NaN 73.266667
3 17.0 0 1.733333 NaN NaN 73.266667
4 14.0 1 2.166667 NaN NaN 73.266667
... ... ... ... ... ... ...
7785 28.0 0 111.900000 111.066667 62.0 111.066667
7786 27.0 1 111.966667 111.600000 62.0 111.600000
7787 26.0 0 112.000000 111.933333 62.0 111.933333
7788 22.0 1 112.866667 112.333333 62.0 112.333333
7789 20.0 0 113.766667 112.866667 62.0 112.866667
[7790 rows x 10 columns],
Unnamed: 0 device_id time_stamp Height \
0 0 70B3D50070001724 2020-05-09 01:07:46.505893 52
1 1 70B3D50070001724 2020-05-09 02:07:46.372718 52
2 2 70B3D50070001724 2020-05-09 03:07:46.259972 52
3 3 70B3D50070001724 2020-05-09 04:07:46.128727 52
4 4 70B3D50070001724 2020-05-09 05:07:46.004955 52
... ... ... ... ...
7799 7799 70B3D50070001724 2021-05-08 17:49:42.078325 120
7800 7800 70B3D50070001724 2021-05-08 18:49:42.019627 122
7801 7801 70B3D50070001724 2021-05-08 19:49:43.468573 116
7802 7802 70B3D50070001724 2021-05-08 20:49:43.335279 120
7803 7803 70B3D50070001724 2021-05-08 21:49:43.207884 120
Temperature Tilt lfilter mov_avg min_avg inter_pol
0 15.0 0 0.866667 NaN NaN 103.066667
1 15.0 0 1.733333 NaN NaN 103.066667
2 14.0 0 2.600000 NaN NaN 103.066667
3 15.0 0 3.466667 NaN NaN 103.066667
4 15.0 0 4.333333 NaN NaN 103.066667
... ... ... ... ... ... ...
7799 23.0 1 114.866667 124.133333 92.0 124.133333
7800 19.0 1 115.100000 124.800000 92.0 124.800000
7801 18.0 1 115.133333 125.133333 92.0 125.133333
7802 18.0 1 115.333333 125.800000 92.0 125.800000
7803 14.0 0 115.533333 126.466667 92.0 126.466667
[7804 rows x 10 columns],
Unnamed: 0 device_id time_stamp Height \
0 0 70B3D50070001726 2020-05-09 00:30:37.051543 66
1 1 70B3D50070001726 2020-05-09 01:30:36.843577 66
2 2 70B3D50070001726 2020-05-09 02:30:36.668140 66
3 3 70B3D50070001726 2020-05-09 04:30:36.260866 70
4 4 70B3D50070001726 2020-05-09 06:30:35.842373 66
... ... ... ... ...
7063 7063 70B3D50070001726 2021-05-08 17:01:34.990787 122
7064 7064 70B3D50070001726 2021-05-08 18:01:34.785983 120
7065 7065 70B3D50070001726 2021-05-08 19:01:34.583706 120
7066 7066 70B3D50070001726 2021-05-08 20:01:35.968216 120
7067 7067 70B3D50070001726 2021-05-08 21:01:35.760639 118
Temperature Tilt lfilter mov_avg min_avg inter_pol
0 18.0 2 1.100000 NaN NaN 104.266667
1 15.0 3 2.200000 NaN NaN 104.266667
2 13.0 3 3.300000 NaN NaN 104.266667
3 13.0 2 4.466667 NaN NaN 104.266667
4 12.0 2 5.566667 NaN NaN 104.266667
... ... ... ... ... ... ...
7063 20.0 3 119.400000 123.866667 108.0 123.866667
7064 18.0 3 119.433333 124.000000 108.0 124.000000
7065 19.0 3 119.400000 124.133333 108.0 124.133333
7066 16.0 3 119.466667 124.266667 108.0 124.266667
7067 13.0 3 119.500000 124.333333 108.0 124.333333
[7068 rows x 10 columns],
Unnamed: 0 device_id time_stamp Height \
0 0 70B3D50070001739 2020-05-09 01:55:25.148156 12
1 1 70B3D50070001739 2020-05-09 02:55:24.935643 12
2 2 70B3D50070001739 2020-05-09 03:55:24.722681 12
3 3 70B3D50070001739 2020-05-09 04:55:24.513137 12
4 4 70B3D50070001739 2020-05-09 05:55:24.383964 12
... ... ... ... ...
7832 7832 70B3D50070001739 2021-05-08 16:25:31.350083 106
7833 7833 70B3D50070001739 2021-05-08 17:25:31.123096 102
7834 7834 70B3D50070001739 2021-05-08 18:25:30.886051 106
7835 7835 70B3D50070001739 2021-05-08 19:25:32.295031 100
7836 7836 70B3D50070001739 2021-05-08 20:25:32.042148 100
Temperature Tilt lfilter mov_avg min_avg inter_pol
0 11.0 0 0.200000 NaN NaN 88.733333
1 10.0 0 0.400000 NaN NaN 88.733333
2 11.0 0 0.600000 NaN NaN 88.733333
3 10.0 0 0.800000 NaN NaN 88.733333
4 9.0 0 1.000000 NaN NaN 88.733333
... ... ... ... ... ... ...
7832 20.0 0 92.200000 103.000000 68.0 103.000000
7833 19.0 0 92.233333 104.133333 68.0 104.133333
7834 16.0 0 92.400000 105.400000 68.0 105.400000
7835 16.0 0 92.466667 106.466667 68.0 106.466667
7836 12.0 0 92.700000 107.533333 68.0 107.533333
[7837 rows x 10 columns],
Unnamed: 0 device_id time_stamp Height \
0 0 70B3D5007000173E 2020-05-09 01:01:07.216028 96
1 1 70B3D5007000173E 2020-05-09 02:01:07.264309 98
2 2 70B3D5007000173E 2020-05-09 03:01:18.913175 98
3 3 70B3D5007000173E 2020-05-09 04:01:07.636712 98
4 4 70B3D5007000173E 2020-05-09 05:01:07.504293 98
... ... ... ... ...
7569 7569 70B3D5007000173E 2021-05-08 13:40:58.079424 80
7570 7570 70B3D5007000173E 2021-05-08 16:40:57.574948 72
7571 7571 70B3D5007000173E 2021-05-08 17:40:57.466938 72
7572 7572 70B3D5007000173E 2021-05-08 18:40:57.309826 72
7573 7573 70B3D5007000173E 2021-05-08 20:40:58.643590 70
Temperature Tilt lfilter mov_avg min_avg inter_pol
0 25.0 3 1.600000 NaN NaN 82.933333
1 24.0 3 3.233333 NaN NaN 82.933333
2 22.0 2 4.866667 NaN NaN 82.933333
3 22.0 2 6.500000 NaN NaN 82.933333
4 20.0 2 8.133333 NaN NaN 82.933333
... ... ... ... ... ... ...
7569 30.0 2 105.566667 103.400000 80.0 103.400000
7570 30.0 1 104.866667 102.533333 72.0 102.533333
7571 32.0 1 104.166667 101.666667 72.0 101.666667
7572 30.0 0 103.433333 100.800000 72.0 100.800000
7573 25.0 0 102.666667 99.866667 70.0 99.866667
[7574 rows x 10 columns],
Unnamed: 0 device_id time_stamp Height \
0 0 70B3D50070001747 2020-05-09 00:48:26.913839 62
1 1 70B3D50070001747 2020-05-09 01:48:26.279385 62
2 2 70B3D50070001747 2020-05-09 02:48:26.086640 62
3 3 70B3D50070001747 2020-05-09 03:48:25.894323 62
4 4 70B3D50070001747 2020-05-09 04:48:25.699732 62
... ... ... ... ...
7462 7462 70B3D50070001747 2021-05-08 16:20:48.596188 136
7463 7463 70B3D50070001747 2021-05-08 17:20:48.388594 136
7464 7464 70B3D50070001747 2021-05-08 18:20:48.244483 136
7465 7465 70B3D50070001747 2021-05-08 19:20:49.592471 136
7466 7466 70B3D50070001747 2021-05-08 22:20:49.213155 136
Temperature Tilt lfilter mov_avg min_avg inter_pol
0 21.0 2 1.033333 NaN NaN 54.533333
1 20.0 1 2.066667 NaN NaN 54.533333
2 19.0 1 3.100000 NaN NaN 54.533333
3 18.0 1 4.133333 NaN NaN 54.533333
4 19.0 1 5.166667 NaN NaN 54.533333
... ... ... ... ... ... ...
7462 31.0 1 98.100000 116.600000 72.0 116.600000
7463 29.0 2 99.033333 118.466667 72.0 118.466667
7464