Python Prophet.plot方法代码示例

# 需要导入模块: from fbprophet import Prophet [as 别名]
# 或者: from fbprophet.Prophet import plot [as 别名]
def run():
    journal = ledger.read_journal("./secret/ledger.dat")
    last_post = None
    amount = 0

    for post in journal.query(""):
        if last_post == None or post.date == last_post.date:
            if str(post.amount.commodity) != "£":
                continue
            amount = amount + post.amount
        else:
            print post.date, ",", amount
            amount = 0
        last_post = post

    df = pd.read_csv('./testing.csv')
    df['y'] = np.multiply(100, df['y'])

    m = Prophet()
    m.fit(df);

    forecast = m.predict(future)
    forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail()

    m.plot(forecast);
    m.plot_components(forecast);

# 需要导入模块: from fbprophet import Prophet [as 别名]
# 或者: from fbprophet.Prophet import plot [as 别名]
#print(forecast1[['ds','yhat','yhat_lower','yhat_upper']])

#%%
## This works and retained the dataframe type
forecast2 = np.exp(forecast1[['yhat','yhat_lower','yhat_upper']])

## Now merge to bring the ds back into the df
## Without the "on" keyword the join key is implicitly the index which is what we're doing here
forecast2 = forecast2.join(forecast1['ds'], how='inner')



#%%
## This works
## This will create a plot that includes Forecasted, C.I.'s, and Actual values
m.plot(forecast1)

#%%
## I think it is unecessary to review exponentiated components 
## Plus the complexity of joining forecast2 with forecast1
m.plot_components(forecast1);

#%%
## It was necessary, in the fill_between, to use a datetime index associated with 
## the first parameter of the function.
## This necessitated converting the existing ds datetime element to an index
pplt.subplots(figsize=(30,10))
forecast2.set_index('ds',inplace=True)

## If using the view_hour data it will be REQUIRED to exponentiate the forecasts (i.e., forecast2)
pplt.plot(view_hour['distinct_freq_sum'], label='Original', color='black');

# 需要导入模块: from fbprophet import Prophet [as 别名]
# 或者: from fbprophet.Prophet import plot [as 别名]
import pandas as pd
import numpy as np
from fbprophet import Prophet

# Prep the dataset

data = pd.read_csv("/home/dusty/Econ8310/DataSets/chicagoBusRiders.csv")
route3 = data[data.route=='3'][['date','rides']]
route3.date = pd.to_datetime(route3.date, infer_datetime_format=True)
route3.columns = [['ds', 'y']]

# Initialize Prophet instance and fit to data

m = Prophet()
m.fit(route3)

# Create timeline for 1 year in future, then generate predictions based on that timeline

future = m.make_future_dataframe(periods=365)
forecast = m.predict(future)

# Create plots of forecast and truth, as well as component breakdowns of the trends

plt = m.plot(forecast)
plt.show()

comp = m.plot_components(forecast)
comp.show()

# 需要导入模块: from fbprophet import Prophet [as 别名]
# 或者: from fbprophet.Prophet import plot [as 别名]
print(forecast['yhat_upper'][0:5])

#%%
## This works and retained the dataframe type
forecast2 = np.exp(forecast1[['yhat','yhat_lower','yhat_upper']])

## Now merge to bring the ds back into the df
## Without the "on" keyword the join key is implicitly the index
forecast2 = forecast2.join(forecast1['ds'], how='inner')

print(forecast1)

#%%
## This works
## This will create a plot that includes Forecasted, C.I.'s, and Actual values
m.plot(forecast2)

#%%
## I think it is unecessary to review exponentiated components 
## Plus the complexity of joining forecast2 with forecast1
m.plot_components(forecast1);

#%%
## It was necessary, in the fill_between, to use a datetime index associated with 
## the first parameter of the function.
## This necessitated converting the existing ds datetime element to an index
pplt.subplots(figsize=(30,10))
forecast2.set_index('ds',inplace=True)

pplt.plot(view_hour['distinct_freq_sum'], label='Original', color='black');
pplt.plot(forecast2.yhat, color='red', label='Forecast');

# 需要导入模块: from fbprophet import Prophet [as 别名]
# 或者: from fbprophet.Prophet import plot [as 别名]
#print(forecast1[['ds','yhat','yhat_lower','yhat_upper']])

#%%
## This works and retained the dataframe type
forecast2 = np.exp(forecast1[['yhat','yhat_lower','yhat_upper']])

## Now merge to bring the ds back into the df
## Without the "on" keyword the join key is implicitly the index which is what we're doing here
forecast2 = forecast2.join(forecast1['ds'], how='inner')



#%%
## This works
## This will create a plot that includes Forecasted, C.I.'s, and Actual values
m.plot(forecast)

#%%
## Save a copy of the plot
fig = m.plot(forecast)  
fig.savefig("/home/steve/forecast_raw.jpeg")

#%%
## I think it is unecessary to review exponentiated components 
## Plus the complexity of joining forecast2 with forecast1
m.plot_components(forecast1);

#%%
## It was necessary, in the fill_between, to use a datetime index associated with 
## the first parameter of the function.
## This necessitated converting the existing ds datetime element to an index