def test_windows_model():
from windows_model import model_t2m_file, model_rh2m_file, model_w10m_file, exract_feature
test_file = "../data/ai_challenger_wf2018_testb1_20180829-20181028.nc"
out_time = pprd_time = '2018-10-28 03'
if (not os.path.exists(data_path_1 + "test.csv")):
transfer_data_to_csv(test_file, data_path_1 + "test.csv")
test_df = load_data(data_path_1 + "test.csv")
# test_df = fill_missing_data(test_df)
for col in [
'psur_obs', 't2m_obs', 'q2m_obs', 'w10m_obs', 'd10m_obs',
'rh2m_obs', 'u10m_obs', 'v10m_obs', 'RAIN_obs'
]:
col_filled = col.split(
'_')[0] + '_M' if 'psur' not in col else 'psfc_M'
test_df[col].fillna(test_df[col_filled], inplace=True) #用超算值填充
test_df_processed = exract_feature(test_df, True)
# 加载模型
t2m_model = pickle.load(open(model_t2m_file, 'rb'))
rh2m_model = pickle.load(open(model_rh2m_file, 'rb'))
w10m_model = pickle.load(open(model_w10m_file, 'rb'))
df_submit = predict(test_df_processed, [t2m_model, rh2m_model, w10m_model],
prd_time) # 预测并打印输出
df_submit.to_csv(output_path + ans_name, index=False)
# 计算分数
anen_file = output_path + ans_name
print_score(fore_file, obs_file, anen_file)
def test_old_days_model3():
from old_days_model3 import model_t2m_file, model_rh2m_file, model_w10m_file, exract_feature
df = load_data("../data/all_data.csv")
df_processed = exract_feature(df)
del df
test_idx = df_processed[lambda x: x.station_date_time.str.split('_').apply(
lambda x: x[1]) == prd_time.replace('-', '').replace(' ', '')].index
df_test = df_processed.loc[test_idx]
del df_processed
# df_test.drop(df_test.columns[33:45], axis=1, inplace=True)
df_test.drop(pd.Index([
'psur_obs', 't2m_obs', 'q2m_obs', 'w10m_obs', 'd10m_obs', 'rh2m_obs',
'u10m_obs', 'v10m_obs', 'RAIN_obs', 't2m_obj', 'rh2m_obj', 'w10m_obj'
]),
axis=1,
inplace=True)
df_test_X = pd.concat(
[df_test[df_test.columns[1]], df_test[df_test.columns[3:]]], axis=1)
# 加载模型
t2m_model = pickle.load(open(model_t2m_file, 'rb'))
rh2m_model = pickle.load(open(model_rh2m_file, 'rb'))
w10m_model = pickle.load(open(model_w10m_file, 'rb'))
df_submit = pd.DataFrame([
[
f'{item[0]}_{int(item[2]):02d}'
for item in df_test.station_date_time.str.split('_')
],
(df_test.t2m_M + t2m_model.predict(df_test_X)).tolist(),
(df_test.rh2m_M + rh2m_model.predict(df_test_X)).tolist(),
(df_test.w10m_M + w10m_model.predict(df_test_X)).tolist(),
]).T.rename(columns={
0: 'FORE_data',
1: 't2m',
2: 'rh2m',
3: 'w10m'
})
df_submit.to_csv(output_path + ans_name, index=False) # 预测并打印输出
# 计算分数
anen_file = output_path + ans_name
print_score(fore_file, obs_file, anen_file)
def test_windows_station_model():
from windows_station_model import model_t2m_file, model_rh2m_file, model_w10m_file, exract_feature
test_file = "../data/ai_challenger_wf2018_testb1_20180829-20181028.nc"
out_time = pprd_time = '2018-10-28 03'
if (not os.path.exists(data_path_1 + "test.csv")):
transfer_data_to_csv(test_file, data_path_1 + "test.csv")
test_df = load_data(data_path_1 + "test.csv")
# test_df = fill_missing_data(test_df)
for col in [
'psur_obs', 't2m_obs', 'q2m_obs', 'w10m_obs', 'd10m_obs',
'rh2m_obs', 'u10m_obs', 'v10m_obs', 'RAIN_obs'
]:
col_filled = col.split(
'_')[0] + '_M' if 'psur' not in col else 'psfc_M'
test_df[col].fillna(test_df[col_filled], inplace=True) #用超算值填充
station_id = [
90001, 90002, 90003, 90004, 90005, 90006, 90007, 90008, 90009, 90010
]
test_station_df = {}
test_df_processed = exract_feature(test_df, True)
for id in station_id:
test_station_df[str(id)] = test_df_processed[
test_df_processed["stations"] == id]
# 加载模型
t2m_model = pickle.load(open(model_t2m_file, 'rb'))
rh2m_model = pickle.load(open(model_rh2m_file, 'rb'))
w10m_model = pickle.load(open(model_w10m_file, 'rb'))
df_submit = predict(test_station_df[str(station_id[0])], [
t2m_model[str(station_id[0])], rh2m_model[str(station_id[0])],
w10m_model[str(station_id[0])]
], prd_time)
for id in station_id[1:]:
df_submit = df_submit.append(
predict(
test_station_df[str(id)],
[t2m_model[str(id)], rh2m_model[str(id)], w10m_model[str(id)]],
prd_time))
df_submit.to_csv(output_path + ans_name, index=False) # 预测并打印输出
# 计算分数
anen_file = output_path + ans_name
print_score(fore_file, obs_file, anen_file)