mirror of
https://gitlab.com/CIEFWorldwideSdnBhd/cief-dashboard.git
synced 2026-08-19 04:24:13 +00:00
shangqian22
86 lines
2.9 KiB
Python
86 lines
2.9 KiB
Python
from app import *
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from mlxtend.regressor import LinearRegression
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def get_predict(df):
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feature_names=df.columns.to_list()[1:]
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target_name=df.columns.to_list()[1]
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# use 60 days to predict
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window_size=60
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x=[]
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y=[]
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for index in range(len(df)):
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if index >= window_size:
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features=np.array([])
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for feature_name in feature_names:
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feature=df.loc[index-window_size:index-1, feature_name].to_numpy()
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features=np.append(features,feature)
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x.append(features)
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target=df.loc[index, target_name]
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y.append(target)
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x=np.array(x)
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y=np.array(y)
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# train
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model = LinearRegression()
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model.fit(x, y)
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# current
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days=60
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plot_df=df.copy()
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plot_df["Type"] = 'Current'
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# predict
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for i in range(days):
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index=len(plot_df)-window_size
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predict_x=np.array([])
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for feature_name in feature_names:
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feature=plot_df[feature_name].iloc[index:].to_numpy()
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predict_x=np.append(predict_x,feature)
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# transform
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predict_x=np.array([predict_x])
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# predict
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predict_y = model.predict(predict_x)
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predict_date=plot_df['Date'].iloc[-1]+pd.Timedelta(1, unit='D')
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predict = pd.DataFrame({'Date':predict_date, feature_names[1]:plot_df[feature_names[1]].mean(), feature_names[0]:predict_y, 'Type':'Predict'})
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plot_df = pd.concat([plot_df,predict])
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plot_df.reset_index(drop=True, inplace=True)
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plot_df.sort_values(['Date'],inplace=True)
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return(plot_df)
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# layout
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ml_list=['Predict Covid-19 Fatal Rate', 'Predict CBM']
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layout = html.Div([
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dcc.Dropdown(
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id='input-ml',
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options=[{'label':a, 'value':i} for i, a in enumerate(ml_list)]),
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dcc.Graph(id='output-predict_fatal_rate')
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])
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# use default option
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@app.callback(Output('input-ml', 'value'),
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Input('data-machine_learning', 'data'))
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def get_ml(data):
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return(0)
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@app.callback(Output('output-predict_fatal_rate', 'figure'),
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Input('input-ml', 'value'),
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State('data-machine_learning', 'data'))
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def update_output(selected_ml, data):
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to_df(data)
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if selected_ml==0:
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covid_world_wide_fatal_rate=data['covid_world_wide_fatal_rate']
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predict_fatal_rate=get_predict(covid_world_wide_fatal_rate)
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fig_predict_fatal_rate = px.line(predict_fatal_rate, x="Date", y='Death percentage', color='Type', title='Fatal Rate Prediction')
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fig_predict_fatal_rate.update_yaxes(tickformat=".2%")
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update_theme(fig_predict_fatal_rate)
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return(fig_predict_fatal_rate)
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elif selected_ml==1:
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cbm_ctn=data['cbm_ctn']
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predict_cbm=get_predict(cbm_ctn)
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fig_predict_cbm = px.line(predict_cbm, x="Date", y='Daily CBM', color='Type', title='CBM Prediction')
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update_theme(fig_predict_cbm)
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return(fig_predict_cbm)
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else:
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raise(PreventUpdate)
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