| from darts import TimeSeries |
| from darts.datasets import ILINetDataset |
| from darts.metrics import mape |
| from darts.models import ExponentialSmoothing |
| from darts.utils.missing_values import fill_missing_values |
| from darts.dataprocessing.transformers import Scaler |
| import matplotlib.pyplot as plt |
| import numpy as np |
| import pandas as pd |
| import os |
|
|
| def load_ILINetDataset(): |
| """ |
| Dataset's Components Descriptions: |
| |
| * % WEIGHTED ILI: Combined state-specific data of patients visit to healthcare providers for ILI reported each week weighted by state population |
| |
| * % UNWEIGHTED ILI: Combined state-specific data of patients visit to healthcare providers for ILI reported each week unweighted by state population |
| |
| * AGE 0-4: Number of patients between 0 and 4 years of age |
| |
| * AGE 25-49: Number of patients between 25 and 49 years of age |
| |
| * AGE 25-64: Number of patients between 25 and 64 years of age |
| |
| * AGE 5-24: Number of patients between 5 and 24 years of age |
| |
| * AGE 50-64: Number of patients between 50 and 64 years of age |
| |
| * AGE 65: Number of patients above (>=65) 65 years of age |
| |
| * ILITOTAL: Total number of ILI patients. For this system, ILI is defined as fever (temperature of 100°F [37.8°C] or greater) and a cough and/or a sore throat |
| |
| * NUM. OF PROVIDERS: Number of outpatient healthcare providers |
| |
| * TOTAL PATIENTS: Total number of patients |
| """ |
| ilidata = ILINetDataset().load().astype(np.float32) |
|
|
| return ilidata |
|
|
|
|
| def preprocess_data(ilidata): |
| ilitotal = ilidata['ILITOTAL'] |
| covariates = ilidata.drop_columns(col_names='ILITOTAL') |
|
|
| |
| log_ili = ilitotal.map(np.log) |
|
|
| |
| pd_log_ili = log_ili.pd_dataframe() |
| pd_log_ili['ILITOTAL'].replace(to_replace=-np.inf, value=np.nan, inplace=True) |
| log_ili = TimeSeries.from_dataframe(pd_log_ili) |
|
|
| log_ili = fill_missing_values(log_ili) |
|
|
| return log_ili |
|
|
|
|
| def train_val_split(ilidata): |
| train_ili, val_ili = ilidata.split_before(0.75) |
|
|
| return train_ili, val_ili |
|
|
|
|
| def scale_train(train_ili): |
| scaler = Scaler() |
| train_ili_scaled = scaler.fit_transform(train_ili) |
|
|
| return train_ili_scaled, scaler |
|
|
|
|
| def train(train_ili_scaled): |
| model = ExponentialSmoothing() |
| model.fit(train_ili_scaled) |
|
|
| return model |
|
|
|
|
| def save_model(model, path): |
| model_name = str(model).split('(')[0] |
| if not os.path.exists(path=path): |
| os.makedirs(path) |
| model.save(os.path.join(path, model_name)) |
| |
| |
|
|
|
|
| def load_model(path): |
| model = ExponentialSmoothing.load(path) |
| |
| return model |
|
|
|
|
| def predict(model, val_ili): |
| preds = model.predict(len(val_ili)) |
|
|
| return preds |
|
|
|
|
| def inverse_scale_predictions(scaled_preds, scaler): |
| preds = scaler.inverse_transform(scaled_preds) |
|
|
| return preds |
|
|
|
|
| def plot_results(train_ili, val_ili, preds): |
| fig, _ = plt.subplots() |
| train_ili.plot(label='train') |
| val_ili.plot(label='val') |
| preds.plot(label='preds') |
| |
| return fig |
|
|
|
|
| def compute_mape(preds, val): |
| metric = mape(val, preds) |
|
|
| return metric |
|
|
|
|
