def test_reperform_chainladder_nowcasts():
current_path = os.path.dirname(os.path.realpath(__file__))
folder = os.path.join(current_path, r"../../../")
start_date = datetime.date(2020, 8, 1)
end_date = datetime.date(2021, 5, 30)
beta = 0.0
method = "L-BFGS-B"
#method = "Powell"
get_lagged_values_func = lambda x: get_lagged_values(folder, x)
corrected_cases_per_day, probabilities = nowcast_cases_per_day(
end_date,
get_lagged_values_func,
get_cases_per_day_from_data_frame,
GitHubRepository(),
beta=beta,
method=method)
avg = pd.Series(corrected_cases_per_day).rolling(window=7).mean().dropna()
avg.to_csv(r"c:\temp\chainladder.csv")
print(avg.iloc[-1])
for i in range(0, (end_date - start_date).days + 1):
dt = start_date + datetime.timedelta(days=i)
corrected_cases_per_day, probabilities = nowcast_cases_per_day(
dt,
get_lagged_values_func,
get_cases_per_day_from_data_frame,
GitHubRepository(),
beta=beta,
method=method)
avg = pd.Series(corrected_cases_per_day).rolling(
window=7).mean().dropna()
print(avg.iloc[-1])
def generate_plot_national_cases_per_day_chainladder(repository, statistics_repository, show_only_last, reporting_lag=0):
df_daily = statistics_repository.get_cases_per_day_from_file()
dt = df_daily.index.unique().max() + datetime.timedelta(days=reporting_lag)
corrected_cases_per_day = chainladder.nowcast_cases_per_day(dt, statistics_repository.get_lagged_values,
statistics_repository.get_cases_per_day_from_data_frame,
repository, beta=0.2, reporting_lag=reporting_lag)[0]
df_updated = pd.Series(data=corrected_cases_per_day, index=df_daily.index[-len(corrected_cases_per_day):])
data_actual = df_daily.dropna()[-show_only_last:]
data_forecast = df_updated.dropna()[-show_only_last:]
data_rolling = df_updated.rolling(window=7).mean().dropna()[-show_only_last:]
data_dict = {"Date": data_actual.index, "Value": data_actual.array }
data_forecast_dict = {"Date": data_forecast.index, "Value": data_forecast.array}
data_rolling_dict = {"Date": data_rolling.index, "Value": data_rolling.array}
source = ColumnDataSource(data_dict)
source_forecast = ColumnDataSource(data_forecast_dict)
source_rolling = ColumnDataSource(data_rolling_dict)
p = figure(x_axis_type="datetime", plot_width=800, plot_height=350)
p.title.text = "Positive tests - actual vs. nowcast"
p.title.align = "center"
p.title.text_font_size = "18px"
p.line('Date', 'Value', source=source, line_dash="dashed", line_width=3, legend_label="Actual (not fully known)", line_color="black")
p.line('Date', 'Value', source=source_forecast, line_dash="dashed", line_width=3, legend_label="Nowcast", line_color=Spectral10[2])
p.line('Date', 'Value', source=source_rolling, line_width=4, legend_label="Nowcast (7-day rolling average)", line_color=Spectral10[0])
p.xaxis.formatter = DatetimeTickFormatter(days="%d/%b", months="%d/%b", hours="%d/%b", minutes="%d/%b")
p.yaxis.axis_label = "Positive tests"
p.legend.location = "top_left"
export_png(p, filename=statistics_repository.folder + r"plots\{country}\COVID-19_daily_cases_plot.png".format(country=statistics_repository.country_code))
def update_measures(df_measures, folder, repository, date_to_run=None):
if df_measures is None or len(df_measures.index) == 0:
dt_last_measure_present = datetime.datetime.min
else:
dt_last_measure_present = df_measures.index[-1].date()
if date_to_run is None:
df_rki_latest = repository.get_dataset(datetime.datetime.today().date())
else:
df_rki_latest = repository.get_dataset(date_to_run + datetime.timedelta(days=REPORTING_LAG))
dt_rki_file = max(df_rki_latest.index).date()
if (dt_last_measure_present + datetime.timedelta(days=REPORTING_LAG)) == dt_rki_file:
return df_measures
df_rivm_previous_day = repository.get_dataset(dt_rki_file - datetime.timedelta(days=1))
df_measures_updated = df_measures.copy()
new_row = pd.Series(dtype="float64")
new_row["net"] = __calculate_measure(df_rki_latest, df_rivm_previous_day, net_increases)
new_row["gross"] = __calculate_measure(df_rki_latest, df_rivm_previous_day, gross_increases)
get_lagged_values_func = lambda x: get_lagged_values(folder, x)
method = "L-BFGS-B"
corrected_cases_per_day, _ = chainladder.nowcast_cases_per_day(dt_rki_file,
get_lagged_values_func,
get_cases_per_day_from_data_frame,
repository, beta=0.0, method=method,
reporting_lag=REPORTING_LAG)
nowcast_chainladder_value = pd.Series(corrected_cases_per_day).rolling(window=7).mean().dropna().iloc[-1]
new_row["nowcast_chain"] = nowcast_chainladder_value
corrected_cases_per_day, _ = chainladder.nowcast_cases_per_day(dt_rki_file,
get_lagged_values_func,
get_cases_per_day_from_data_frame,
repository, beta=0.2, method=method,
reporting_lag=REPORTING_LAG)
nowcast_chainladder_value_beta_0_2 = pd.Series(corrected_cases_per_day).rolling(window=7).mean().dropna().iloc[-1]
new_row["nowcast_chain_0_2"] = nowcast_chainladder_value_beta_0_2
new_row.name = (dt_rki_file - datetime.timedelta(days=REPORTING_LAG)).strftime("%Y-%m-%d")
df_measures_updated = df_measures_updated.append(new_row)
df_measures_updated.index = pd.to_datetime(df_measures_updated.index, format="%Y-%m-%d")
return df_measures_updated
def test_nowcast_powell_vs_lbfgsb():
dt = datetime.date(2020, 10, 1)
dir_path = os.path.dirname(os.path.realpath(__file__))
folder = os.path.join(dir_path, '../../')
get_lagged_values_func = lambda x: get_lagged_values(folder, x)
corrected_cases_per_day_powell, probabilities_powell =\
nowcast_cases_per_day(dt, get_lagged_values_func, get_cases_per_day_from_data_frame, FixtureRepository, 31, beta=0.0, method="Powell")
corrected_cases_per_day_lbfgsb, probabilities_lbfgsb =\
nowcast_cases_per_day(dt, get_lagged_values_func, get_cases_per_day_from_data_frame, FixtureRepository, 31, beta=0.0, method="L-BFGS-B")
max_probability_difference = max(
abs(np.array(probabilities_powell) - np.array(probabilities_lbfgsb)))
assert max_probability_difference < 0.0003
corrected_cases_differences = max(
abs(
np.array(corrected_cases_per_day_powell) -
np.array(corrected_cases_per_day_lbfgsb)))
assert corrected_cases_differences < 5.0
def test_reperform_chainladder_nowcasts():
current_path = os.path.dirname(os.path.realpath(__file__))
folder = os.path.join(current_path, r"../../../")
today_date = datetime.date(2020, 12, 25)
start_date = datetime.date(2020, 8, 1)
end_date = datetime.date(2020, 12, 24)
method = "L-BFGS-B"
repo = RkiAndGitHubRepositoryWithCaching(today_date)
get_lagged_values_func = lambda x: get_lagged_values(folder, x)
for i in range(0, (end_date - start_date).days + 1):
dt = start_date + datetime.timedelta(days=i)
corrected_cases_per_day, probabilities = nowcast_cases_per_day(
dt,
get_lagged_values_func,
get_cases_per_day_from_data_frame,
repo,
beta=0.2,
method=method)
avg_0_2 = pd.Series(corrected_cases_per_day).rolling(
window=7).mean().dropna()
corrected_cases_per_day, probabilities = nowcast_cases_per_day(
dt,
get_lagged_values_func,
get_cases_per_day_from_data_frame,
repo,
beta=0.0,
method=method)
avg_0_0 = pd.Series(corrected_cases_per_day).rolling(
window=7).mean().dropna()
print("{zero},{zeropointtwo}".format(zero=avg_0_0.iloc[-1],
zeropointtwo=avg_0_2.iloc[-1]))
def test_update_nowcasts():
current_path = os.path.dirname(os.path.realpath(__file__))
folder = os.path.join(current_path, r"../../../")
dt_rki_file = datetime.date(2020, 12, 19)
rki_repository = GitHubRepository()
get_lagged_values_func = lambda x: get_lagged_values(folder, x)
corrected_cases_per_day, probs = chainladder.nowcast_cases_per_day(dt_rki_file,
get_lagged_values_func,
get_cases_per_day_from_data_frame,
rki_repository, beta=0.2)
probs = probs
def generate_plots_chainladder(repository, statistics_repository, start_date, skip_last, reporting_lag=0):
df_daily = statistics_repository.get_cases_per_day_from_file()
dt = df_daily.index.unique().max() + datetime.timedelta(days=reporting_lag)
nowcast_cases_per_day = chainladder.nowcast_cases_per_day(dt, statistics_repository.get_lagged_values,
statistics_repository.get_cases_per_day_from_data_frame,
repository, beta=0.2, reporting_lag=reporting_lag)[0]
df_updated = pd.Series(data=nowcast_cases_per_day, index=df_daily.index[-len(nowcast_cases_per_day):])
df_measures = statistics_repository.get_measures()
nowcast_same_day_chain_0_2 = df_measures["nowcast_chain_0_2"]
nowcast_same_day_chain = df_measures["nowcast_chain"]
gross = df_measures["gross"]
data_actual = df_daily.dropna().rolling(window=7).mean().dropna()[start_date:]
data_nowcast_chain_0_2 = df_updated.rolling(window=7).mean().dropna()[start_date:]
data_nowcast_chain_0_2_same_day = nowcast_same_day_chain_0_2.dropna()[start_date:]
data_nowcast_chain_same_day = nowcast_same_day_chain.dropna()[start_date:]
data_gross = gross.rolling(window=7).mean().dropna()[start_date:]
data_actual_dict = {"Date": data_actual.index, "Value": data_actual.array}
data_nowcast_dict = {"Date": data_nowcast_chain_0_2.index, "Value": data_nowcast_chain_0_2.array}
data_nowcast_same_day_dict = {"Date": data_nowcast_chain_0_2_same_day.index, "Value": data_nowcast_chain_0_2_same_day.array}
data_gross_dict = {"Date": data_gross.index, "Value": data_gross.array}
source_actual = ColumnDataSource(data_actual_dict)
source_nowcast = ColumnDataSource(data_nowcast_dict)
source_nowcast_same_day = ColumnDataSource(data_nowcast_same_day_dict)
source_gross = ColumnDataSource(data_gross_dict)
p = figure(x_axis_type="datetime", plot_width=800, plot_height=350)
p.title.text = "7-day rolling average of positive tests"
p.title.align = "center"
p.title.text_font_size = "18px"
p.line('Date', 'Value', source=source_actual, line_dash="dashed", line_width=4, legend_label="Actual (not fully known)", line_color='black')
p.line('Date', 'Value', source=source_nowcast, line_width=4, legend_label="Nowcast", line_color=Spectral10[0])
p.line('Date', 'Value', source=source_nowcast_same_day, line_dash="solid", line_dash_offset=2, line_width=4, legend_label="Nowcast (same-day)", line_color=Spectral10[2])
p.line('Date', 'Value', source=source_gross, line_width=4, legend_label="Gross", line_color=Spectral10[1])
p.xaxis.formatter = DatetimeTickFormatter(days="%d/%b", months="%d/%b", hours="%d/%b", minutes="%d/%b")
p.yaxis.axis_label = "Positive tests"
p.legend.location = "top_left"
export_png(p, filename=statistics_repository.folder + r"plots\{country}\COVID-19_daily_cases_nowcast_performance.png".format(country=statistics_repository.country_code))
# Calculate differences
number_of_elements = len(data_actual) - skip_last
data_gross_diff = (data_gross - data_actual)[:number_of_elements]
data_nowcast_chain_0_2_diff = (data_nowcast_chain_0_2_same_day - data_actual)[:number_of_elements]
data_nowcast_chain_diff = (data_nowcast_chain_same_day - data_actual)[:number_of_elements]
source_gross_diff = ColumnDataSource({"Date": data_gross_diff.index, "Value": data_gross_diff.array})
source_nowcast_chain_0_2_diff = ColumnDataSource({"Date": data_nowcast_chain_0_2_diff.index, "Value": data_nowcast_chain_0_2_diff.array})
source_nowcast_chain_diff = ColumnDataSource({"Date": data_nowcast_chain_diff.index, "Value": data_nowcast_chain_diff.array})
p = figure(x_axis_type="datetime", plot_width=800, plot_height=350)
p.title.text = "Error in same-day nowcast of 7-day rolling average of positive tests"
p.title.align = "center"
p.title.text_font_size = "18px"
p.line('Date', 'Value', source=source_gross_diff, line_width=3, legend_label="Gross", line_color=Spectral10[1])
p.line('Date', 'Value', source=source_nowcast_chain_diff, line_width=3, line_dash="solid", legend_label="Nowcast (β=0.0)", line_color=Spectral10[9])
p.line('Date', 'Value', source=source_nowcast_chain_0_2_diff, line_width=3, line_dash="solid", legend_label="Nowcast (β=0.2)", line_color=Spectral10[2])
p.xaxis.formatter = DatetimeTickFormatter(days="%d/%b", months="%d/%b", hours="%d/%b", minutes="%d/%b")
p.yaxis.axis_label = "Error in positive tests"
p.legend.location = "top_left"
export_png(p, filename=statistics_repository.folder + r"plots\{country}\COVID-19_daily_cases_nowcast_error.png".format(country=statistics_repository.country_code))
def update_measures(df_measures, folder, repository, date_to_run=None):
dt_last_measure_present = df_measures.index[-1].date()
if date_to_run is None:
df_rivm_latest = repository.get_dataset(
datetime.datetime.today().date())
else:
df_rivm_latest = repository.get_dataset(date_to_run)
dt_rivm_file = max(df_rivm_latest.index).date()
if dt_last_measure_present == dt_rivm_file:
return df_measures
df_rivm_previous_day = get_rivm_file_historical(dt_rivm_file -
datetime.timedelta(days=1))
ggd_regions = get_ggd_regions()
df_measures_updated = df_measures.copy()
new_row = pd.Series(dtype="float64")
for ggd_region in ggd_regions["Municipal_health_service"]:
new_row["net_" + ggd_region] = __calculate_measure(
df_rivm_latest, df_rivm_previous_day, net_increases, ggd_region)
new_row["gross_" + ggd_region] = __calculate_measure(
df_rivm_latest, df_rivm_previous_day, gross_increases, ggd_region)
new_row["net"] = __calculate_measure(df_rivm_latest, df_rivm_previous_day,
net_increases)
new_row["gross"] = __calculate_measure(df_rivm_latest,
df_rivm_previous_day,
gross_increases)
nowcast_value = forecast_daily_cases(
folder, maximum_lag=14).rolling(window=7).mean().dropna().iloc[-1]
new_row["nowcast"] = nowcast_value
nowcast_value_beta_0_2 = forecast_daily_cases(
folder, beta=0.2,
maximum_lag=14).rolling(window=7).mean().dropna().iloc[-1]
new_row["nowcast_0_2"] = nowcast_value_beta_0_2
get_lagged_values_func = lambda x: get_lagged_values(folder, x)
method = "L-BFGS-B"
corrected_cases_per_day, _ = chainladder.nowcast_cases_per_day(
dt_rivm_file,
get_lagged_values_func,
get_cases_per_day_from_data_frame,
repository,
beta=0.0,
method=method)
nowcast_chainladder_value = pd.Series(corrected_cases_per_day).rolling(
window=7).mean().dropna().iloc[-1]
new_row["nowcast_chain"] = nowcast_chainladder_value
corrected_cases_per_day, _ = chainladder.nowcast_cases_per_day(
dt_rivm_file,
get_lagged_values_func,
get_cases_per_day_from_data_frame,
repository,
beta=0.2,
method=method)
nowcast_chainladder_value_beta_0_2 = pd.Series(
corrected_cases_per_day).rolling(window=7).mean().dropna().iloc[-1]
new_row["nowcast_chain_0_2"] = nowcast_chainladder_value_beta_0_2
new_row.name = dt_rivm_file.strftime("%Y-%m-%d")
df_measures_updated = df_measures_updated.append(new_row)
df_measures_updated.index = pd.to_datetime(df_measures_updated.index,
format="%Y-%m-%d")
return df_measures_updated
