def test_light(self):
"""Test that the light_plot function is accessing the correct data."""
test_df = pd.read_csv(mapping_funcs.DATA_DIR + "/2017.csv")
test_out = mlpredict.light_plot(test_df, TEST_OUTPUT)
self.assertEqual(len(test_out), 8,
"The dataframe counts for light are incorrect.")
def test_mlpredict(self):
"""Test that the different functions being called in mlpredict.py produce
the plots it's supposed to in the location that it's supposed to."""
year_plot_list = []
for yr in range(2013, 2018):
year_plot_list.append(pd.read_csv(mapping_funcs.DATA_DIR + '/{}.csv'.format(yr)).shape[0])
_ = mlpredict.year_plot(year_plot_list, TEST_OUTPUT)
df_2017 = pd.read_csv(mapping_funcs.DATA_DIR + "/2017.csv")
_ = mlpredict.month_plot(df_2017, TEST_OUTPUT)
_ = mlpredict.weekday_plot(df_2017, TEST_OUTPUT)
_ = mlpredict.weather_plot(df_2017, TEST_OUTPUT)
_ = mlpredict.road_plot(df_2017, TEST_OUTPUT)
_ = mlpredict.light_plot(df_2017, TEST_OUTPUT)
_ = mlpredict.ml_prediction(df_2017, TEST_OUTPUT)
self.assertEqual((os.path.isfile(TEST_OUTPUT + '/year_plot.png') and
os.path.isfile(TEST_OUTPUT + '/month_plot.png') and
os.path.isfile(TEST_OUTPUT + 'weekday_plot.png') and
os.path.isfile(TEST_OUTPUT + 'weather_plot.png') and
os.path.isfile(TEST_OUTPUT + 'road_plot.png') and
os.path.isfile(TEST_OUTPUT + 'light_plot.png') and
os.path.isfile(TEST_OUTPUT + 'weather_factor_importance.png')),
True,
"Missing one or more outputs")
def generate_ml(self, year):
merged_data = pd.read_csv(mapping_funcs.DATA_DIR +
'/{}.csv'.format(year))
mlpredict.month_plot(merged_data)
mlpredict.weekday_plot(merged_data)
mlpredict.weather_plot(merged_data)
mlpredict.road_plot(merged_data)
mlpredict.light_plot(merged_data)
mlpredict.ml_prediction(merged_data)
year_plot_list = []
for year in range(2013, 2018):
year_plot_list.append(
pd.read_csv(mapping_funcs.DATA_DIR +
'/{}.csv'.format(year)).shape[0])
mlpredict.year_plot(year_plot_list)
pass
def generate_ml(year):
"""
generate statistical analysis plots in the outputs folder
"""
merged_data = pd.read_csv(mapping_funcs.DATA_DIR + '/{}.csv'.format(year))
mlpredict.month_plot(merged_data)
mlpredict.weekday_plot(merged_data)
mlpredict.weather_plot(merged_data)
mlpredict.road_plot(merged_data)
mlpredict.light_plot(merged_data)
mlpredict.ml_prediction(merged_data)
year_plot_list = []
for years in range(2013, 2018):
year_plot_list.append(
pd.read_csv(mapping_funcs.DATA_DIR +
'/{}.csv'.format(years)).shape[0])
mlpredict.year_plot(year_plot_list)
def test_mlpredict():
year_plot_list = []
for yr in range(2013, 2018):
year_plot_list.append(
pd.read_csv(mapping_funcs.DATA_DIR + '/2013.csv').shape[0])
mlpredict.year_plot(year_plot_list, TEST_OUTPUT)
df_2017 = pd.read_csv(mapping_funcs.DATA_DIR + "/2017.csv")
mlpredict.month_plot(df_2017, TEST_OUTPUT)
mlpredict.weekday_plot(df_2017, TEST_OUTPUT)
mlpredict.weather_plot(df_2017, TEST_OUTPUT)
mlpredict.road_plot(df_2017, TEST_OUTPUT)
mlpredict.light_plot(df_2017, TEST_OUTPUT)
mlpredict.ml_prediction(df_2017, TEST_OUTPUT)
unittest.assertEqual(
(os.path.isfile(TEST_OUTPUT + '/year_plot.png')
and os.path.isfile(TEST_OUTPUT + '/month_plot.png')
and os.path.isfile(TEST_OUTPUT + 'weekday_plot.png')
and os.path.isfile(TEST_OUTPUT + 'weather_plot.png')
and os.path.isfile(TEST_OUTPUT + 'road_plot.png')
and os.path.isfile(TEST_OUTPUT + 'light_plot.png')
and os.path.isfile(TEST_OUTPUT + 'weather_factor_importance.png')),
True, "Missing one or more outputs")