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test.py
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test.py
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import os as os
from lib import ReadCsv
from lib import ReadConfig
from lib import ReadData
from lib import NetworkModel
from lib import ModelMetrics
from lib import SeriesPlot
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import keras
from datetime import datetime
from pandas.plotting import scatter_matrix
config = ReadConfig.ReadConfig()
config_data = config.read_config(os.path.join("config", "config.json"))
reader = ReadData.ReadData()
all_data = reader.readClimateFiles(config_data)
plt.scatter(all_data["local_date"], all_data["Hs"])
plt.show()
##sns.pairplot(all_data)
subset = all_data[["local_date",
"Hs",
"Hmax",
"Tz",
"Tp",
"DirTpTRUE",
"SST",
"Evapotranspiration_mm",
"Rain_mm",
"PanEvaporation_mm",
"MaximumTemperature_C",
"MaxRelativeHumidity_pc",
"MinRelativeHumidity_pc",
"Avg10mWindSpeed_m_sec",
"SolarRadiation_MJ_sqm"]]
C = subset[subset.columns[1:14]].corr()
sns.heatmap(C)
plt.show()
modeller = NetworkModel.NetworkModel()
train, valid, test = modeller.partition_data(subset)
x_cols = ["Evapotranspiration_mm",
"Rain_mm",
"PanEvaporation_mm",
"MaximumTemperature_C",
"MaxRelativeHumidity_pc",
"MinRelativeHumidity_pc",
"Avg10mWindSpeed_m_sec",
"SolarRadiation_MJ_sqm"]
y_cols = ["Hs",
"Hmax",
"Tz",
"Tp",
"DirTpTRUE",
"SST"]
train_x = train[x_cols]
train_y = train[y_cols]
valid_x = valid[x_cols]
valid_y = valid[y_cols]
test_x = test[x_cols]
test_y = test[y_cols]
num_inputs = train_x.shape[1]
num_outputs = train_y.shape[1]
epochs = 500
logdir="logs/scalars/model1" + datetime.now().strftime("%Y%m%d-%H%M%S")
tensorboard_callback = keras.callbacks.TensorBoard(log_dir=logdir)
model = modeller.model_dense(num_inputs, num_outputs)
modeller.compile_model(model, keras.optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False),
"mean_squared_error",
["acc",
"mae"])
# model1 overfits at around 150 epochs
modeller.fit_model(
model,
train_x.values,
train_y.values,
valid_x.values,
valid_y.values,
num_epochs=epochs,
batch_size=32,
callback_list=[tensorboard_callback])
loss, accuracy, mae = model.evaluate(x=test_x.values,
y=test_y.values)
print("Loss: "+str(loss))
print("Accuracy: "+str(accuracy))
print("Mean Absolute Error: "+str(mae))
y_sim = model.predict(x=test_x.values)
y_sim.shape
metrics = ModelMetrics.ModelMetrics()
test_dates = test["local_date"]
pairs = list(zip(test.columns[1:7], range(0, 6)))
all_metrics = metrics.report_all_metrics('SimpleDense', pairs, test, y_sim)
series_plot = SeriesPlot.SeriesPlot()
series_plot.plot_series(pairs, test['local_date'].values, test, y_sim)
series_plot.plot_histograms(pairs, test, y_sim)
all_data2 = reader.readClimateFiles(config_data, add_wavelets=True, wavelet_cols=y_cols)
all_data2.dtypes
## we construct a new model using predictors including the wavelet coefficients.
## and wavelet reconstruction for the t-1 record in order to predict t.
subset2 = all_data2[["local_date",
"Hs",
"Hmax",
"Tz",
"Tp",
"DirTpTRUE",
"SST",
"Evapotranspiration_mm",
"Rain_mm",
"PanEvaporation_mm",
"MaximumTemperature_C",
"MaxRelativeHumidity_pc",
"MinRelativeHumidity_pc",
"Avg10mWindSpeed_m_sec",
"SolarRadiation_MJ_sqm",
"Hs_A1",
"Hs_C1",
"Hs_C2",
"Hs_C3",
"Hmax_A1",
"Hmax_C1",
"Hmax_C2",
"Hmax_C3",
"Tz_A1",
"Tz_C1",
"Tz_C2",
"Tz_C3",
"Tp_A1",
"Tp_C1",
"Tp_C2",
"Tp_C3",
"DirTpTRUE_A1",
"DirTpTRUE_C1",
"DirTpTRUE_C2",
"DirTpTRUE_C3",
"SST_A1",
"SST_C1",
"SST_C2",
"SST_C3"]]
train2, valid2, test2 = modeller.partition_data(subset2)
x_cols = ["Evapotranspiration_mm",
"Rain_mm",
"PanEvaporation_mm",
"MaximumTemperature_C",
"MaxRelativeHumidity_pc",
"MinRelativeHumidity_pc",
"Avg10mWindSpeed_m_sec",
"SolarRadiation_MJ_sqm",
"Hs_A1",
"Hs_C1",
"Hs_C2",
"Hs_C3",
"Hmax_A1",
"Hmax_C1",
"Hmax_C2",
"Hmax_C3",
"Tz_A1",
"Tz_C1",
"Tz_C2",
"Tz_C3",
"Tp_A1",
"Tp_C1",
"Tp_C2",
"Tp_C3",
"DirTpTRUE_A1",
"DirTpTRUE_C1",
"DirTpTRUE_C2",
"DirTpTRUE_C3",
"SST_A1",
"SST_C1",
"SST_C2",
"SST_C3"]
y_cols = ["Hs",
"Hmax",
"Tz",
"Tp",
"DirTpTRUE",
"SST"]
train_x = train2[x_cols]
train_y = train2[y_cols]
valid_x = valid2[x_cols]
valid_y = valid2[y_cols]
test_x = test2[x_cols]
test_y = test2[y_cols]
num_inputs = train_x.shape[1]
num_outputs = train_y.shape[1]
logdir="logs/scalars/model2" + datetime.now().strftime("%Y%m%d-%H%M%S")
tensorboard_callback = keras.callbacks.TensorBoard(log_dir=logdir)
model2 = modeller.model_dense(num_inputs, num_outputs)
modeller.compile_model(model2, keras.optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False),
"mean_squared_error",
["acc",
"mae"])
## model2 overfits at > 90 epochs
modeller.fit_model(
model2,
train_x.values,
train_y.values,
valid_x.values,
valid_y.values,
num_epochs=epochs,
batch_size=32,
callback_list=[tensorboard_callback])
loss, accuracy, mae = model2.evaluate(x=test_x.values,
y=test_y.values)
print("Loss: "+str(loss))
print("Accuracy: "+str(accuracy))
print("Mean Absolute Error: "+str(mae))
y_sim2 = model2.predict(x=test_x.values)
pairs = list(zip(test2.columns[1:7], range(0,6)))
all_metrics2 = metrics.report_all_metrics('WaveletDense', pairs, test2, y_sim2)
series_plot = SeriesPlot.SeriesPlot()
series_plot.plot_series(pairs, test2['local_date'].values, test2, y_sim2)
series_plot.plot_histograms(pairs, test2, y_sim2)
print(all_metrics2)
## Investivate modelling using min/max standardisation.
features = ["Evapotranspiration_mm",
"Rain_mm",
"PanEvaporation_mm",
"MaximumTemperature_C",
"MaxRelativeHumidity_pc",
"MinRelativeHumidity_pc",
"Avg10mWindSpeed_m_sec",
"SolarRadiation_MJ_sqm",
"Hs_A1",
"Hs_C1",
"Hs_C2",
"Hs_C3",
"Hmax_A1",
"Hmax_C1",
"Hmax_C2",
"Hmax_C3",
"Tz_A1",
"Tz_C1",
"Tz_C2",
"Tz_C3",
"Tp_A1",
"Tp_C1",
"Tp_C2",
"Tp_C3",
"DirTpTRUE_A1",
"DirTpTRUE_C1",
"DirTpTRUE_C2",
"DirTpTRUE_C3",
"SST_A1",
"SST_C1",
"SST_C2",
"SST_C3",
"Hs",
"Hmax",
"Tz",
"Tp",
"DirTpTRUE",
"SST"]
## The min and max need to be kept for the ability to rescale after training.
data_min = all_data2[features].min()
data_max = all_data2[features].max()
data_delta = data_max[features] - data_min[features]
data_scaled = (all_data2[features] - data_min)/data_delta
data_scaled['local_date'] = all_data2['local_date']
## We now train on the scaled data.
train3, valid3, test3 = modeller.partition_data(data_scaled)
train_x = train3[x_cols]
train_y = train3[y_cols]
valid_x = valid3[x_cols]
valid_y = valid3[y_cols]
test_x = test3[x_cols]
test_y = test3[y_cols]
num_inputs = train_x.shape[1]
num_outputs = train_y.shape[1]
logdir="logs/scalars/model3" + datetime.now().strftime("%Y%m%d-%H%M%S")
tensorboard_callback = keras.callbacks.TensorBoard(log_dir=logdir)
model3 = modeller.model_dense(num_inputs, num_outputs)
modeller.compile_model(model3, keras.optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False),
"mean_squared_error",
["acc",
"mae"])
## Model 3 overfits somewhere near 60 epochs
modeller.fit_model(
model3,
train_x.values,
train_y.values,
valid_x.values,
valid_y.values,
num_epochs=epochs,
batch_size=32,
callback_list=[tensorboard_callback])
loss, accuracy, mae = model3.evaluate(x=test_x.values,
y=test_y.values)
print("Loss: "+str(loss))
print("Accuracy: "+str(accuracy))
print("Mean Absolute Error: "+str(mae))
y_sim3 = model3.predict(x=test_x.values)
y_sim3_scaled = pd.DataFrame(y_sim3, columns=y_cols) * data_delta[y_cols] + data_min[y_cols]
obs3_scaled = test_y * data_delta[y_cols] + data_min[y_cols]
test_dates = test3["local_date"]
pairs = list(zip(['Hs', 'Hmax', 'Tz', 'Tp', 'DirTpTRUE', 'SST'], range(0,6)))
all_metrics3 = metrics.report_all_metrics('ScaledWaveletDense', pairs, obs3_scaled, y_sim3_scaled.values)
series_plot = SeriesPlot.SeriesPlot()
series_plot.plot_series(pairs, test3['local_date'].values, obs3_scaled, y_sim3_scaled.values)
series_plot.plot_histograms(pairs, obs3_scaled, y_sim3_scaled.values)
print(all_metrics3)
# a fourth model using sigmoid activation
logdir="logs/scalars/model4" + datetime.now().strftime("%Y%m%d-%H%M%S")
tensorboard_callback = keras.callbacks.TensorBoard(log_dir=logdir)
model4 = modeller.model_dense(num_inputs, num_outputs, hidden_activation='sigmoid')
modeller.compile_model(model4, keras.optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False),
"mean_squared_error",
["acc",
"mae"])
## model4 overfits somewhere above 190 epochs
modeller.fit_model(
model4,
train_x.values,
train_y.values,
valid_x.values,
valid_y.values,
num_epochs=epochs,
batch_size=32,
callback_list=[tensorboard_callback])
loss, accuracy, mae = model4.evaluate(x=test_x.values,
y=test_y.values)
print("Loss: "+str(loss))
print("Accuracy: "+str(accuracy))
print("Mean Absolute Error: "+str(mae))
y_sim4 = model4.predict(x=test_x.values)
y_sim4_scaled = pd.DataFrame(y_sim4, columns=y_cols) * data_delta[y_cols] + data_min[y_cols]
all_metrics4 = metrics.report_all_metrics('ScaledWaveletDenseSigmoid', pairs, obs3_scaled, y_sim4_scaled.values)
series_plot = SeriesPlot.SeriesPlot()
series_plot.plot_series(pairs, test3['local_date'].values, obs3_scaled, y_sim4_scaled.values)
series_plot.plot_histograms(pairs, obs3_scaled, y_sim4_scaled.values)
print(all_metrics4)
## fifth model using tanh activation
logdir="logs/scalars/model5" + datetime.now().strftime("%Y%m%d-%H%M%S")
tensorboard_callback = keras.callbacks.TensorBoard(log_dir=logdir)
model5 = modeller.model_dense(num_inputs, num_outputs, hidden_activation='tanh')
modeller.compile_model(model5, keras.optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False),
"mean_squared_error",
["acc",
"mae"])
## Model 5 seems to continue to decrease for 1000 epochs
## have not tested longer durations.
modeller.fit_model(
model5,
train_x.values,
train_y.values,
valid_x.values,
valid_y.values,
num_epochs=epochs,
batch_size=32,
callback_list=[tensorboard_callback])
loss, accuracy, mae = model5.evaluate(x=test_x.values,
y=test_y.values)
print("Loss: "+str(loss))
print("Accuracy: "+str(accuracy))
print("Mean Absolute Error: "+str(mae))
y_sim5 = model5.predict(x=test_x.values)
y_sim5_scaled = pd.DataFrame(y_sim5, columns=y_cols) * data_delta[y_cols] + data_min[y_cols]
all_metrics5 = metrics.report_all_metrics('ScaledWaveletDenseTanh', pairs, obs3_scaled, y_sim5_scaled.values)
series_plot = SeriesPlot.SeriesPlot()
series_plot.plot_series(pairs, test3['local_date'].values, obs3_scaled, y_sim5_scaled.values)
series_plot.plot_histograms(pairs, obs3_scaled, y_sim5_scaled.values)
print(all_metrics5)
metric_data = pd.concat([all_metrics, all_metrics2, all_metrics3, all_metrics4, all_metrics5])
metrics = ['R2','agreement_d', 'efficiency_E', 'RMSE','MAE','percentPeakDeviation']
for metric in metrics:
bars = sns.barplot(x='Model', y=metric, data=metric_data, hue='Property')
for item in bars.get_xticklabels():
item.set_rotation(45)
plt.show()
import numpy as np
a, b = np.corrcoef(y_sim3_scaled["SST"].values, obs3_scaled['SST'].values)
np.corrcoef(y_sim3_scaled["Hs"].values, obs3_scaled['Hs'].values)
np.corrcoef(y_sim3_scaled["Hmax"].values, obs3_scaled['Hmax'].values)
## Reviewing modwt components of input data.
plt.subplot(411)
plt.scatter(all_data2['local_date'], all_data2['SST_A1'], label='A1')
plt.legend()
plt.subplot(412)
plt.scatter(all_data2['local_date'], all_data2['SST_C1'], label='C1')
plt.legend()
plt.subplot(413)
plt.scatter(all_data2['local_date'], all_data2['SST_C2'], label='C2')
plt.legend()
plt.subplot(414)
plt.scatter(all_data2['local_date'], all_data2['SST_C3'], label='C3')
plt.legend()
plt.show()
plt.subplot(411)
plt.scatter(all_data2['local_date'], all_data2['Hs_A1'], label='A1')
plt.legend()
plt.subplot(412)
plt.scatter(all_data2['local_date'], all_data2['Hs_C1'], label='C1')
plt.legend()
plt.subplot(413)
plt.scatter(all_data2['local_date'], all_data2['Hs_C2'], label='C2')
plt.legend()
plt.subplot(414)
plt.scatter(all_data2['local_date'], all_data2['Hs_C3'], label='C3')
plt.legend()
plt.show()
plt.subplot(411)
plt.scatter(all_data2['local_date'], all_data2['Hmax_A1'], label='A1')
plt.legend()
plt.subplot(412)
plt.scatter(all_data2['local_date'], all_data2['Hmax_C1'], label='C1')
plt.legend()
plt.subplot(413)
plt.scatter(all_data2['local_date'], all_data2['Hmax_C2'], label='C2')
plt.legend()
plt.subplot(414)
plt.scatter(all_data2['local_date'], all_data2['Hmax_C3'], label='C3')
plt.legend()
plt.show()
## Inspect wavelet decomposition of SST.
import pywt
A, C3, C2, C1 = pywt.wavedec(all_data2['SST'].values, 'db3', level=3)
plt.subplot(511)
plt.plot(all_data2['SST'].values, label='SST', color='red')
plt.legend()
plt.subplot(512)
plt.plot(A, label='A1')
plt.legend()
plt.subplot(513)
plt.plot(C1, label='C1')
plt.legend()
plt.subplot(514)
plt.plot(C2, label='C2')
plt.legend()
plt.subplot(515)
plt.plot(C3, label='C3')
plt.legend()
plt.show()