Datasets:
HenryAI
/
KerasCodeExamples.txt

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\"wv (m/s)\",
\"max. wv (m/s)\",
\"wd (deg)\",
]
colors = [
\"blue\",
\"orange\",
\"green\",
\"red\",
\"purple\",
\"brown\",
\"pink\",
\"gray\",
\"olive\",
\"cyan\",
]
date_time_key = \"Date Time\"
def show_raw_visualization(data):
time_data = data[date_time_key]
fig, axes = plt.subplots(
nrows=7, ncols=2, figsize=(15, 20), dpi=80, facecolor=\"w\", edgecolor=\"k\"
)
for i in range(len(feature_keys)):
key = feature_keys[i]
c = colors[i % (len(colors))]
t_data = data[key]
t_data.index = time_data
t_data.head()
ax = t_data.plot(
ax=axes[i // 2, i % 2],
color=c,
title=\"{} - {}\".format(titles[i], key),
rot=25,
)
ax.legend([titles[i]])
plt.tight_layout()
show_raw_visualization(df)
png
This heat map shows the correlation between different features.
def show_heatmap(data):
plt.matshow(data.corr())
plt.xticks(range(data.shape[1]), data.columns, fontsize=14, rotation=90)
plt.gca().xaxis.tick_bottom()
plt.yticks(range(data.shape[1]), data.columns, fontsize=14)
cb = plt.colorbar()
cb.ax.tick_params(labelsize=14)
plt.title(\"Feature Correlation Heatmap\", fontsize=14)
plt.show()
show_heatmap(df)
png
Data Preprocessing
Here we are picking ~300,000 data points for training. Observation is recorded every 10 mins, that means 6 times per hour. We will resample one point per hour since no drastic change is expected within 60 minutes. We do this via the sampling_rate argument in timeseries_dataset_from_array utility.
We are tracking data from past 720 timestamps (720/6=120 hours). This data will be used to predict the temperature after 72 timestamps (72/6=12 hours).
Since every feature has values with varying ranges, we do normalization to confine feature values to a range of [0, 1] before training a neural network. We do this by subtracting the mean and dividing by the standard deviation of each feature.
71.5 % of the data will be used to train the model, i.e. 300,693 rows. split_fraction can be changed to alter this percentage.
The model is shown data for first 5 days i.e. 720 observations, that are sampled every hour. The temperature after 72 (12 hours * 6 observation per hour) observation will be used as a label.
split_fraction = 0.715
train_split = int(split_fraction * int(df.shape[0]))
step = 6
past = 720
future = 72
learning_rate = 0.001
batch_size = 256
epochs = 10
def normalize(data, train_split):
data_mean = data[:train_split].mean(axis=0)
data_std = data[:train_split].std(axis=0)
return (data - data_mean) / data_std
We can see from the correlation heatmap, few parameters like Relative Humidity and Specific Humidity are redundant. Hence we will be using select features, not all.
print(
\"The selected parameters are:\",
\", \".join([titles[i] for i in [0, 1, 5, 7, 8, 10, 11]]),
)
selected_features = [feature_keys[i] for i in [0, 1, 5, 7, 8, 10, 11]]
features = df[selected_features]
features.index = df[date_time_key]
features.head()
features = normalize(features.values, train_split)