def main():
from data.model.records import KEYS_ALL_HEALTH_RECORDS
from trainer.dnn_trainer import DNNTrainer
from auth.fitbit_authenticator import FitbitAuthenticator
from datetime import timedelta
from fitbit import Fitbit
from data.data_loader import DataLoader
from data.provider.fitbit_data_provider import FitbitDataProvider
import fitbit as fitbit
from datetime import date
import pandas as pd
import tensorflow as tf
from data.dataset_generator import DatasetGenerator
from plotter import plotter
auth = FitbitAuthenticator("../configs/fitbit_auth_config.json")
auth.authenticate_in_browser()
fitbit = fitbit.Fitbit(auth.client_id,
auth.client_secret,
access_token=auth.access_token,
refresh_token=auth.refresh_token,
system=Fitbit.METRIC)
start_date = date(year=2020, month=1, day=1)
end_date = date.today() - timedelta(days=1)
data_provider = FitbitDataProvider(fitbit, start_date, end_date)
loader = DataLoader(data_provider)
loader.generate_records()
plotter.plot_all_records(loader.records)
loader.write_to_csv("../data/raw/health_records.csv")
pd.set_option('display.max_columns', 15)
data_frame = pd.read_csv("../data/raw/health_records.csv")
data_frame = data_frame.fillna(data_frame.mean())
data_frame = data_frame.drop(columns=['record_date'])
# Check dataset stats
print(data_frame.describe().transpose())
last_record = data_frame.tail(1)
print('Most recent health record:\n', last_record)
# Set the modified values for my personal goal
my_goal_changes = {'weight': 65}
# Merging the most recent record with the modified values
my_goal = last_record.copy()
for key, value in my_goal_changes.items():
my_goal[key] = value
print('My goal merged with most recent health record:\n', my_goal)
record_keys = KEYS_ALL_HEALTH_RECORDS
# Skip the modified keys, there is no need to predict overwritten records
for key in my_goal_changes.keys():
record_keys.remove(key)
prediction_results = 'Results for my goal {}'.format(my_goal_changes)
tf.keras.backend.clear_session()
tf.random.set_seed(60)
for key in record_keys:
dataset = DatasetGenerator(data_frame, target_feature=key)
dataset.split(train_ratio=0.75, valid_ratio=0.15, test_ratio=0.10)
trainer = DNNTrainer(dataset, key)
model = trainer.train()
# Drop the target feature that we are train for
my_goal_input = my_goal.drop(columns=[key])
prediction = model.predict(my_goal_input)
feature_prediction = 'Predicted {} value for my goal: {}'.format(
key, prediction)
prediction_results += '\n' + feature_prediction
print(prediction_results)
class TestDataLoader:
TEST_CSV_FILE = 'test_health_records.csv'
@pytest.fixture(autouse=True)
def setup(self, mocker: MockerFixture):
data_provider = MockDataProvider()
mock = mocker.patch.object(data_provider, 'get_heart_records')
mock.return_value = {
date(year=2020, month=1, day=1): {
'resting_heart': 60
}
}
mock = mocker.patch.object(data_provider, 'get_body_records')
mock.return_value = {
date(year=2020, month=1, day=1): {
'weight': 75,
'fat': 15.12,
'bmi': 21.12
},
date(year=2020, month=1, day=2): {
'weight': 74,
'fat': 14.12,
'bmi': 20.12
}
}
mock = mocker.patch.object(data_provider, 'get_sleep_records')
mock.return_value = {
date(year=2020, month=1, day=1): {
'sleep_duration': 480,
'sleep_efficiency': 80
}
}
mock = mocker.patch.object(data_provider, 'get_activity_records')
mock.return_value = {
date(year=2020, month=1, day=2): {
'total_calories': 1600,
'active_calories': 400,
'sedentary_minutes': 600,
'lightly_active_minutes': 10,
'fairly_active_minutes': 10,
'highly_active_minutes': 10
}
}
self.data_loader = DataLoader(data_provider)
yield self.data_loader
if os.path.exists(self.TEST_CSV_FILE):
os.remove(self.TEST_CSV_FILE)
def test_generate_records(self):
self.data_loader.generate_records()
assert self.data_loader.records == {
date(year=2020, month=1, day=1): {
'resting_heart': 60,
'weight': 75,
'fat': 15.12,
'bmi': 21.12,
'sleep_duration': 480,
'sleep_efficiency': 80,
'total_calories': None,
'active_calories': None,
'sedentary_minutes': None,
'lightly_active_minutes': None,
'fairly_active_minutes': None,
'highly_active_minutes': None
},
date(year=2020, month=1, day=2): {
'resting_heart': None,
'weight': 74,
'fat': 14.12,
'bmi': 20.12,
'sleep_duration': None,
'sleep_efficiency': None,
'total_calories': 1600,
'active_calories': 400,
'sedentary_minutes': 600,
'lightly_active_minutes': 10,
'fairly_active_minutes': 10,
'highly_active_minutes': 10
}
}
def test_write_to_csv(self):
self.data_loader.generate_records()
self.data_loader.write_to_csv("test_health_records.csv")
with open(self.TEST_CSV_FILE, newline='') as csv_file:
reader = csv.reader(csv_file)
row_first = next(reader)
assert row_first == [
'record_date', 'resting_heart', 'weight', 'fat', 'bmi',
'sleep_duration', 'sleep_efficiency', 'total_calories',
'active_calories', 'sedentary_minutes',
'lightly_active_minutes', 'fairly_active_minutes',
'highly_active_minutes'
]
row_second = next(reader)
assert row_second == [
'2020-01-01', '60', '75', '15.12', '21.12', '480', '80', '',
'', '', '', '', ''
]
row_third = next(reader)
assert row_third == [
'2020-01-02', '', '74', '14.12', '20.12', '', '', '1600',
'400', '600', '10', '10', '10'
]
