In [1]:
%matplotlib inline
import dateutil.parser
import datetime
import matplotlib.dates as mdates
import numpy as np
import pandas as pd
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.contrib import learn
from pymongo import MongoClient
from bson.objectid import ObjectId
from sklearn.metrics import mean_squared_error
from lstm import lstm_model
from data_processing import generate_data, load_csvdata
In [2]:
LOG_DIR = './ops_logs/lstm_weather'
TIMESTEPS = 10
RNN_LAYERS = [{'num_units': 5}]
DENSE_LAYERS = [10, 10]
TRAINING_STEPS = 100000
BATCH_SIZE = 100
PRINT_STEPS = TRAINING_STEPS / 100
In [3]:
# downloaded weather data from http://www.ncdc.noaa.gov/qclcd/QCLCD
def load_weather_frame(filename):
#load the weather data and make a date
data_raw = pd.read_csv(filename, dtype={'Time': str, 'Date': str})
data_raw['WetBulbCelsius'] = data_raw['WetBulbCelsius'].astype(float)
times = []
for index, row in data_raw.iterrows():
_t = datetime.time(int(row['Time'][:2]), int(row['Time'][:-2]), 0) #2153
_d = datetime.datetime.strptime( row['Date'], "%Y%m%d" ) #20150905
times.append(datetime.datetime.combine(_d, _t))
data_raw['_time'] = pd.Series(times, index=data_raw.index)
df = pd.DataFrame(data_raw, columns=['_time','WetBulbCelsius'])
return df.set_index('_time')
In [4]:
# scale values to reasonable values and convert to float
data_weather = load_weather_frame("data/QCLCD_PDX_20150901.csv")
X, y = load_csvdata(data_weather, TIMESTEPS, seperate=False)
In [5]:
regressor = learn.SKCompat(learn.Estimator(
model_fn=lstm_model(
TIMESTEPS,
RNN_LAYERS,
DENSE_LAYERS
),
model_dir=LOG_DIR
))
INFO:tensorflow:Using default config.
INFO:tensorflow:Using config: {'_model_dir': None, '_save_checkpoints_secs': 600, '_num_ps_replicas': 0, '_keep_checkpoint_max': 5, '_tf_random_seed': None, '_task_type': None, '_environment': 'local', '_is_chief': True, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f8d909fb910>, '_tf_config': gpu_options {
per_process_gpu_memory_fraction: 1.0
}
, '_num_worker_replicas': 0, '_task_id': 0, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_evaluation_master': '', '_keep_checkpoint_every_n_hours': 10000, '_master': ''}
In [6]:
# create a lstm instance and validation monitor
validation_monitor = learn.monitors.ValidationMonitor(X['val'], y['val'],
every_n_steps=PRINT_STEPS,
early_stopping_rounds=1000)
regressor.fit(X['train'], y['train'],
monitors=[validation_monitor],
batch_size=BATCH_SIZE,
steps=TRAINING_STEPS)
predicted = regressor.predict(X['test'])
WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/monitors.py:267: __init__ (from tensorflow.contrib.learn.python.learn.monitors) is deprecated and will be removed after 2016-12-05.
Instructions for updating:
Monitors are deprecated. Please use tf.train.SessionRunHook.
WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/models.py:107: mean_squared_error_regressor (from tensorflow.contrib.learn.python.learn.ops.losses_ops) is deprecated and will be removed after 2016-12-01.
Instructions for updating:
Use `tf.contrib.losses.mean_squared_error` and explicit logits computation.
WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/ops/losses_ops.py:39: mean_squared_error (from tensorflow.contrib.losses.python.losses.loss_ops) is deprecated and will be removed after 2016-12-30.
Instructions for updating:
Use tf.losses.mean_squared_error instead.
WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/contrib/losses/python/losses/loss_ops.py:539: compute_weighted_loss (from tensorflow.contrib.losses.python.losses.loss_ops) is deprecated and will be removed after 2016-12-30.
Instructions for updating:
Use tf.losses.compute_weighted_loss instead.
WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/contrib/losses/python/losses/loss_ops.py:151: add_loss (from tensorflow.contrib.losses.python.losses.loss_ops) is deprecated and will be removed after 2016-12-30.
Instructions for updating:
Use tf.losses.add_loss instead.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Restoring parameters from ./ops_logs/lstm_weather/model.ckpt-100000
INFO:tensorflow:Saving checkpoints for 100001 into ./ops_logs/lstm_weather/model.ckpt.
INFO:tensorflow:loss = 0.198107, step = 100001
WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/monitors.py:662: calling evaluate (from tensorflow.contrib.learn.python.learn.estimators.estimator) with y is deprecated and will be removed after 2016-12-01.
Instructions for updating:
Estimator is decoupled from Scikit Learn interface by moving into
separate class SKCompat. Arguments x, y and batch_size are only
available in the SKCompat class, Estimator will only accept input_fn.
Example conversion:
est = Estimator(...) -> est = SKCompat(Estimator(...))
WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/monitors.py:662: calling evaluate (from tensorflow.contrib.learn.python.learn.estimators.estimator) with x is deprecated and will be removed after 2016-12-01.
Instructions for updating:
Estimator is decoupled from Scikit Learn interface by moving into
separate class SKCompat. Arguments x, y and batch_size are only
available in the SKCompat class, Estimator will only accept input_fn.
Example conversion:
est = Estimator(...) -> est = SKCompat(Estimator(...))
WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/models.py:107: mean_squared_error_regressor (from tensorflow.contrib.learn.python.learn.ops.losses_ops) is deprecated and will be removed after 2016-12-01.
Instructions for updating:
Use `tf.contrib.losses.mean_squared_error` and explicit logits computation.
WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/ops/losses_ops.py:39: mean_squared_error (from tensorflow.contrib.losses.python.losses.loss_ops) is deprecated and will be removed after 2016-12-30.
Instructions for updating:
Use tf.losses.mean_squared_error instead.
WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/contrib/losses/python/losses/loss_ops.py:539: compute_weighted_loss (from tensorflow.contrib.losses.python.losses.loss_ops) is deprecated and will be removed after 2016-12-30.
Instructions for updating:
Use tf.losses.compute_weighted_loss instead.
WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/contrib/losses/python/losses/loss_ops.py:151: add_loss (from tensorflow.contrib.losses.python.losses.loss_ops) is deprecated and will be removed after 2016-12-30.
Instructions for updating:
Use tf.losses.add_loss instead.
INFO:tensorflow:Starting evaluation at 2017-06-23-02:58:10
INFO:tensorflow:Restoring parameters from ./ops_logs/lstm_weather/model.ckpt-100001
INFO:tensorflow:Finished evaluation at 2017-06-23-02:58:10
INFO:tensorflow:Saving dict for global step 100001: global_step = 100001, loss = 0.147549
WARNING:tensorflow:Skipping summary for global_step, must be a float or np.float32.
INFO:tensorflow:Validation (step 100001): loss = 0.147549, global_step = 100001
INFO:tensorflow:global_step/sec: 73.8054
INFO:tensorflow:loss = 0.232113, step = 100101 (1.356 sec)
INFO:tensorflow:global_step/sec: 383.278
INFO:tensorflow:loss = 0.245043, step = 100201 (0.261 sec)
INFO:tensorflow:global_step/sec: 523.21
INFO:tensorflow:loss = 0.29345, step = 100301 (0.191 sec)
INFO:tensorflow:global_step/sec: 519.604
INFO:tensorflow:loss = 0.198635, step = 100401 (0.192 sec)
INFO:tensorflow:global_step/sec: 548.673
INFO:tensorflow:loss = 0.174668, step = 100501 (0.182 sec)
INFO:tensorflow:global_step/sec: 502.058
INFO:tensorflow:loss = 0.233283, step = 100601 (0.199 sec)
INFO:tensorflow:global_step/sec: 569.35
INFO:tensorflow:loss = 0.197275, step = 100701 (0.176 sec)
INFO:tensorflow:global_step/sec: 547.549
INFO:tensorflow:loss = 0.192045, step = 100801 (0.183 sec)
INFO:tensorflow:global_step/sec: 552.059
INFO:tensorflow:loss = 0.259165, step = 100901 (0.181 sec)
INFO:tensorflow:global_step/sec: 557.603
INFO:tensorflow:loss = 0.207421, step = 101001 (0.180 sec)
INFO:tensorflow:global_step/sec: 543.425
INFO:tensorflow:loss = 0.191576, step = 101101 (0.184 sec)
INFO:tensorflow:global_step/sec: 543.354
INFO:tensorflow:loss = 0.235745, step = 101201 (0.184 sec)
INFO:tensorflow:global_step/sec: 507.746
INFO:tensorflow:loss = 0.179108, step = 101301 (0.197 sec)
INFO:tensorflow:global_step/sec: 534.208
INFO:tensorflow:loss = 0.209948, step = 101401 (0.187 sec)
INFO:tensorflow:global_step/sec: 555.929
INFO:tensorflow:loss = 0.239748, step = 101501 (0.180 sec)
INFO:tensorflow:global_step/sec: 541.642
INFO:tensorflow:loss = 0.20222, step = 101601 (0.185 sec)
INFO:tensorflow:global_step/sec: 524.727
INFO:tensorflow:loss = 0.206883, step = 101701 (0.191 sec)
INFO:tensorflow:global_step/sec: 546.654
INFO:tensorflow:loss = 0.183927, step = 101801 (0.183 sec)
INFO:tensorflow:global_step/sec: 558.101
INFO:tensorflow:loss = 0.197307, step = 101901 (0.179 sec)
INFO:tensorflow:global_step/sec: 517.703
INFO:tensorflow:loss = 0.270146, step = 102001 (0.193 sec)
INFO:tensorflow:global_step/sec: 545.557
INFO:tensorflow:loss = 0.281118, step = 102101 (0.183 sec)
INFO:tensorflow:global_step/sec: 478.985
INFO:tensorflow:loss = 0.225038, step = 102201 (0.209 sec)
INFO:tensorflow:global_step/sec: 574.026
INFO:tensorflow:loss = 0.201878, step = 102301 (0.174 sec)
INFO:tensorflow:global_step/sec: 510.999
INFO:tensorflow:loss = 0.27336, step = 102401 (0.196 sec)
INFO:tensorflow:global_step/sec: 547.813
INFO:tensorflow:loss = 0.17788, step = 102501 (0.182 sec)
INFO:tensorflow:global_step/sec: 566.746
INFO:tensorflow:loss = 0.199143, step = 102601 (0.177 sec)
INFO:tensorflow:global_step/sec: 546.006
INFO:tensorflow:loss = 0.217345, step = 102701 (0.183 sec)
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INFO:tensorflow:loss = 0.191515, step = 102801 (0.214 sec)
INFO:tensorflow:global_step/sec: 525.188
INFO:tensorflow:loss = 0.179646, step = 102901 (0.191 sec)
INFO:tensorflow:global_step/sec: 545.983
INFO:tensorflow:loss = 0.298378, step = 103001 (0.183 sec)
INFO:tensorflow:global_step/sec: 508.2
INFO:tensorflow:loss = 0.166484, step = 103101 (0.197 sec)
INFO:tensorflow:global_step/sec: 531.149
INFO:tensorflow:loss = 0.170688, step = 103201 (0.188 sec)
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INFO:tensorflow:loss = 0.229359, step = 103301 (0.183 sec)
INFO:tensorflow:global_step/sec: 501.213
INFO:tensorflow:loss = 0.201423, step = 103401 (0.199 sec)
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INFO:tensorflow:global_step/sec: 441.4
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INFO:tensorflow:global_step/sec: 496.396
INFO:tensorflow:loss = 0.227246, step = 193001 (0.201 sec)
INFO:tensorflow:global_step/sec: 537.057
INFO:tensorflow:loss = 0.260381, step = 193101 (0.186 sec)
INFO:tensorflow:global_step/sec: 545.598
INFO:tensorflow:loss = 0.20475, step = 193201 (0.183 sec)
INFO:tensorflow:global_step/sec: 522.947
INFO:tensorflow:loss = 0.220157, step = 193301 (0.191 sec)
INFO:tensorflow:global_step/sec: 522.881
INFO:tensorflow:loss = 0.153409, step = 193401 (0.191 sec)
INFO:tensorflow:global_step/sec: 496.504
INFO:tensorflow:loss = 0.191605, step = 193501 (0.201 sec)
INFO:tensorflow:global_step/sec: 495.504
INFO:tensorflow:loss = 0.198203, step = 193601 (0.202 sec)
INFO:tensorflow:global_step/sec: 536.866
INFO:tensorflow:loss = 0.196749, step = 193701 (0.186 sec)
INFO:tensorflow:global_step/sec: 545.593
INFO:tensorflow:loss = 0.188217, step = 193801 (0.183 sec)
INFO:tensorflow:global_step/sec: 507.869
INFO:tensorflow:loss = 0.194318, step = 193901 (0.197 sec)
INFO:tensorflow:global_step/sec: 518.358
INFO:tensorflow:loss = 0.229695, step = 194001 (0.193 sec)
INFO:tensorflow:global_step/sec: 510.285
INFO:tensorflow:loss = 0.254785, step = 194101 (0.196 sec)
INFO:tensorflow:global_step/sec: 501.94
INFO:tensorflow:loss = 0.28525, step = 194201 (0.199 sec)
INFO:tensorflow:global_step/sec: 499.753
INFO:tensorflow:loss = 0.198431, step = 194301 (0.204 sec)
INFO:tensorflow:global_step/sec: 525.166
INFO:tensorflow:loss = 0.252969, step = 194401 (0.187 sec)
INFO:tensorflow:global_step/sec: 517.754
INFO:tensorflow:loss = 0.223127, step = 194501 (0.193 sec)
INFO:tensorflow:global_step/sec: 523.919
INFO:tensorflow:loss = 0.229531, step = 194601 (0.191 sec)
INFO:tensorflow:global_step/sec: 546.442
INFO:tensorflow:loss = 0.197176, step = 194701 (0.183 sec)
INFO:tensorflow:global_step/sec: 539.544
INFO:tensorflow:loss = 0.159416, step = 194801 (0.185 sec)
INFO:tensorflow:global_step/sec: 558.219
INFO:tensorflow:loss = 0.23909, step = 194901 (0.179 sec)
INFO:tensorflow:global_step/sec: 534.41
INFO:tensorflow:loss = 0.236096, step = 195001 (0.187 sec)
INFO:tensorflow:global_step/sec: 531.618
INFO:tensorflow:loss = 0.230091, step = 195101 (0.188 sec)
INFO:tensorflow:global_step/sec: 547.744
INFO:tensorflow:loss = 0.244952, step = 195201 (0.182 sec)
INFO:tensorflow:global_step/sec: 527.936
INFO:tensorflow:loss = 0.196662, step = 195301 (0.190 sec)
INFO:tensorflow:global_step/sec: 530.548
INFO:tensorflow:loss = 0.298503, step = 195401 (0.189 sec)
INFO:tensorflow:global_step/sec: 494.697
INFO:tensorflow:loss = 0.305266, step = 195501 (0.201 sec)
INFO:tensorflow:global_step/sec: 527.037
INFO:tensorflow:loss = 0.189876, step = 195601 (0.190 sec)
INFO:tensorflow:global_step/sec: 512.618
INFO:tensorflow:loss = 0.227363, step = 195701 (0.195 sec)
INFO:tensorflow:global_step/sec: 542.479
INFO:tensorflow:loss = 0.238926, step = 195801 (0.184 sec)
INFO:tensorflow:global_step/sec: 553.752
INFO:tensorflow:loss = 0.215529, step = 195901 (0.180 sec)
INFO:tensorflow:global_step/sec: 540.535
INFO:tensorflow:loss = 0.160458, step = 196001 (0.185 sec)
INFO:tensorflow:global_step/sec: 551.225
INFO:tensorflow:loss = 0.222865, step = 196101 (0.181 sec)
INFO:tensorflow:global_step/sec: 512.808
INFO:tensorflow:loss = 0.272122, step = 196201 (0.195 sec)
INFO:tensorflow:global_step/sec: 535.306
INFO:tensorflow:loss = 0.221531, step = 196301 (0.187 sec)
INFO:tensorflow:global_step/sec: 526.053
INFO:tensorflow:loss = 0.269222, step = 196401 (0.190 sec)
INFO:tensorflow:global_step/sec: 543.552
INFO:tensorflow:loss = 0.199574, step = 196501 (0.184 sec)
INFO:tensorflow:global_step/sec: 544.336
INFO:tensorflow:loss = 0.187047, step = 196601 (0.184 sec)
INFO:tensorflow:global_step/sec: 512.971
INFO:tensorflow:loss = 0.210368, step = 196701 (0.195 sec)
INFO:tensorflow:global_step/sec: 532.584
INFO:tensorflow:loss = 0.167186, step = 196801 (0.188 sec)
INFO:tensorflow:global_step/sec: 520.779
INFO:tensorflow:loss = 0.306849, step = 196901 (0.192 sec)
INFO:tensorflow:global_step/sec: 534.359
INFO:tensorflow:loss = 0.210014, step = 197001 (0.187 sec)
INFO:tensorflow:global_step/sec: 542.005
INFO:tensorflow:loss = 0.237138, step = 197101 (0.184 sec)
INFO:tensorflow:global_step/sec: 552.227
INFO:tensorflow:loss = 0.236061, step = 197201 (0.181 sec)
INFO:tensorflow:global_step/sec: 557.454
INFO:tensorflow:loss = 0.191805, step = 197301 (0.179 sec)
INFO:tensorflow:global_step/sec: 534.617
INFO:tensorflow:loss = 0.206435, step = 197401 (0.187 sec)
INFO:tensorflow:global_step/sec: 524.007
INFO:tensorflow:loss = 0.218886, step = 197501 (0.190 sec)
INFO:tensorflow:global_step/sec: 555.96
INFO:tensorflow:loss = 0.245679, step = 197601 (0.180 sec)
INFO:tensorflow:global_step/sec: 550.107
INFO:tensorflow:loss = 0.164701, step = 197701 (0.182 sec)
INFO:tensorflow:global_step/sec: 549.514
INFO:tensorflow:loss = 0.256764, step = 197801 (0.182 sec)
INFO:tensorflow:global_step/sec: 510.276
INFO:tensorflow:loss = 0.211446, step = 197901 (0.196 sec)
INFO:tensorflow:global_step/sec: 526.857
INFO:tensorflow:loss = 0.196332, step = 198001 (0.190 sec)
INFO:tensorflow:global_step/sec: 504.086
INFO:tensorflow:loss = 0.266162, step = 198101 (0.198 sec)
INFO:tensorflow:global_step/sec: 531.107
INFO:tensorflow:loss = 0.238168, step = 198201 (0.188 sec)
INFO:tensorflow:global_step/sec: 516.82
INFO:tensorflow:loss = 0.168849, step = 198301 (0.194 sec)
INFO:tensorflow:global_step/sec: 518.715
INFO:tensorflow:loss = 0.204961, step = 198401 (0.193 sec)
INFO:tensorflow:global_step/sec: 530.282
INFO:tensorflow:loss = 0.254896, step = 198501 (0.189 sec)
INFO:tensorflow:global_step/sec: 522.062
INFO:tensorflow:loss = 0.217431, step = 198601 (0.192 sec)
INFO:tensorflow:global_step/sec: 544.236
INFO:tensorflow:loss = 0.164767, step = 198701 (0.184 sec)
INFO:tensorflow:global_step/sec: 525.472
INFO:tensorflow:loss = 0.254297, step = 198801 (0.190 sec)
INFO:tensorflow:global_step/sec: 532.822
INFO:tensorflow:loss = 0.204908, step = 198901 (0.188 sec)
INFO:tensorflow:global_step/sec: 533.086
INFO:tensorflow:loss = 0.233216, step = 199001 (0.187 sec)
INFO:tensorflow:global_step/sec: 511.509
INFO:tensorflow:loss = 0.239908, step = 199101 (0.195 sec)
INFO:tensorflow:global_step/sec: 543.529
INFO:tensorflow:loss = 0.214542, step = 199201 (0.184 sec)
INFO:tensorflow:global_step/sec: 532.654
INFO:tensorflow:loss = 0.245222, step = 199301 (0.188 sec)
INFO:tensorflow:global_step/sec: 541.439
INFO:tensorflow:loss = 0.210748, step = 199401 (0.185 sec)
INFO:tensorflow:global_step/sec: 530.785
INFO:tensorflow:loss = 0.267614, step = 199501 (0.188 sec)
INFO:tensorflow:global_step/sec: 521.368
INFO:tensorflow:loss = 0.198423, step = 199601 (0.192 sec)
INFO:tensorflow:global_step/sec: 543.747
INFO:tensorflow:loss = 0.193262, step = 199701 (0.184 sec)
INFO:tensorflow:global_step/sec: 533.205
INFO:tensorflow:loss = 0.230032, step = 199801 (0.188 sec)
INFO:tensorflow:global_step/sec: 518.731
INFO:tensorflow:loss = 0.225829, step = 199901 (0.193 sec)
INFO:tensorflow:Saving checkpoints for 200000 into ./ops_logs/lstm_weather/model.ckpt.
INFO:tensorflow:Loss for final step: 0.201507.
WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/models.py:107: mean_squared_error_regressor (from tensorflow.contrib.learn.python.learn.ops.losses_ops) is deprecated and will be removed after 2016-12-01.
Instructions for updating:
Use `tf.contrib.losses.mean_squared_error` and explicit logits computation.
WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/ops/losses_ops.py:39: mean_squared_error (from tensorflow.contrib.losses.python.losses.loss_ops) is deprecated and will be removed after 2016-12-30.
Instructions for updating:
Use tf.losses.mean_squared_error instead.
WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/contrib/losses/python/losses/loss_ops.py:539: compute_weighted_loss (from tensorflow.contrib.losses.python.losses.loss_ops) is deprecated and will be removed after 2016-12-30.
Instructions for updating:
Use tf.losses.compute_weighted_loss instead.
WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/contrib/losses/python/losses/loss_ops.py:151: add_loss (from tensorflow.contrib.losses.python.losses.loss_ops) is deprecated and will be removed after 2016-12-30.
Instructions for updating:
Use tf.losses.add_loss instead.
INFO:tensorflow:Restoring parameters from ./ops_logs/lstm_weather/model.ckpt-200000
In [7]:
#not used in this example but used for seeing deviations
rmse = np.sqrt(((predicted - y['test']) ** 2).mean(axis=0))
score = mean_squared_error(predicted, y['test'])
print ("MSE: %f" % score)
MSE: 0.446543
In [8]:
# plot the data
all_dates = data_weather.index.get_values()
fig, ax = plt.subplots(1)
fig.autofmt_xdate()
predicted_values = predicted.flatten() #already subset
predicted_dates = all_dates[len(all_dates)-len(predicted_values):len(all_dates)]
predicted_series = pd.Series(predicted_values, index=predicted_dates)
plot_predicted, = ax.plot(predicted_series, label='predicted (c)')
test_values = y['test'].flatten()
test_dates = all_dates[len(all_dates)-len(test_values):len(all_dates)]
test_series = pd.Series(test_values, index=test_dates)
plot_test, = ax.plot(test_series, label='2015 (c)')
xfmt = mdates.DateFormatter('%b %d %H')
ax.xaxis.set_major_formatter(xfmt)
# ax.fmt_xdata = mdates.DateFormatter('%Y-%m-%d %H')
plt.title('PDX Weather Predictions for 2016 vs 2015')
plt.legend(handles=[plot_predicted, plot_test])
plt.show()
In [ ]:
Content source: mouradmourafiq/tensorflow-lstm-regression
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