Common Steps for using Machine Learning Model

Common Steps to Use a Machine Learning Model

1) Load the data & Split data into X & y 

import pandas   
pandas.read_csv("./data/1.csv")
X = df.drop("target", axis=1) # using all columns besides target
y = df["target"] #  predicting y using X

2) Model selection  & Split the data into training and test sets

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y,test_size=0.2)

3) Instantiate the Model  

from sklearn.ensemble import RandomForestClassifier
m= RandomForestClassifier(n_estimators=50)

4) Fit the model using function 

m.fit(X_train,y_train); 

5) Make prediction

ypreds=m.predict(X_test)

7) To Evaluate Model use  score() function on test and train data

m.score(X_test, y_test)
m.score(X_train,y_train)

8) To improve the model by changing its hyperParameters

from sklearn.model_selection import cross_val_score

import numpy as np

# Use different numbers of  n_estimators  as hyperparameter 

np.random.seed(40)
for i in range(10, 100, 5):
print(f"Trying model with {i} estimators...")
m= RandomForestClassifier(n_estimators=i).fit(X_train, y_train)
print(f"Model accruacy on test data set: {m.score(X_test, y_test)}")

9) Save trained model to file using pickle

import pickle
pickle.dump(m, open("My_Random_forest_model.pkl", "wb"))

10) Load a saved model and make a prediction on a single example
saved_model = pickle.load(open("My_Random_forest_model.pkl", "rb"))
saved_model.score(X_train,y_train)
#######################Example of  a Machine Model###########
Items_List =final_df.ITEMS.unique() ##ITEMS is the column in dataframe having the Item_names
for x in range(0,1): #x in Items_List
    y='Item_names'
    fc=get_test()
    print(fc)
    fc=fc[['ds', 'yhat', 'yhat_lower', 'yhat_upper']]
    fc['Region_NAME']=y    
    fc.to_csv('./Future_ITEMS/'+y+'.csv')#Future_ITEMS is a folder
	
	
def get_test():
  data=final_df#.loc[final_df['ITEMS']==x]
  param_grid = {'changepoint_prior_scale': [0.001, 0.01, 0.1, 0.5],'seasonality_prior_scale': [0.01, 0.1, 1.0, 10.0],}
  # Generate all combinations of parameters
  all_params = [dict(zip(param_grid.keys(), v)) for v in itertools.product(*param_grid.values())]
  rmses = []  # Store the RMSEs for each params here
    for params in all_params:
            m = Prophet(**params).fit(data)  # Fit model with given params
            df_cv = cross_validation(m, initial='180 days', period='90 days', horizon = '365 days')
            print(df_cv)
            df_p = performance_metrics(df_cv, rolling_window=1)
            print(df_p)
            rmses.append(df_p['rmse'].values[0])
# Find the best parameters
tuning_results = pandas.DataFrame(all_params)
tuning_results['rmse'] = rmses
best_params = all_params[np.argmin(rmses)]
# Initiate model with best parameters
m = Prophet(changepoint_prior_scale=best_params['changepoint_prior_scale'], 
seasonality_prior_scale=best_params['seasonality_prior_scale']).fit(data)
# make prediction
future = m.make_future_dataframe(periods=1460,freq='D') 
forecast = m.predict(future)
return forecast

Forcasting by Using Model in Python

 Time Series Forecasting with ML Model

Following are the steps we need to following to Apply the model on the data. Below is the small example.

Step 1

Create the instance of the Prophet class 

Step 2 

Call  the Methods 

A) fit  method 

B) predict methods

Note:-The input to Prophet is always a dataframe with two columns: ds and y

a) The ds (datestamp) column having format like YYYY-MM-DD  or YYYY-MM-DD HH:MM:SS 

        b) y column should be numeric on which prediction is made

        c) settings for the forecasting procedure are passed into the constructor

Sample Example Code

import pandas as pd

from prophet import Prophet

df =pd.read_csv('1.csv')

print(df)

m = Prophet()

m.fit(df)

cast_future = m.make_future_dataframe(periods=365)

print(cast_future)

fcast = m.predict(cast_future)

print(fcast)

fcast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']]

fig1 = m.plot(fcast)

fig2 = m.plot_components(fcast)

Practical Example for the  Model

import pandas as pd

import os,sys

source_folder = r".\\data"

data2=pd.DataFrame()

for file in os.listdir(source_folder):

    data= pd.read_csv(".\\data\\"+file+" ", dtype=str)

    data2=data2.append(data)

    print(file,"has been appended")

data2.head()

data2=data2.drop('Unnamed: 0',axis=1)

data2.head()

data2['test']=data2['HR'] .apply(lambda x: '{0:0>4}'.format(x))

data2.head()

data2['ds']=data2['DT'].str.cat(data2['test'],sep=" ")

data2.head()

data2['ds']=pd.to_datetime(data2['ds'])

data2['EXTRA'] = data2['EXTRA'].astype('float')

data2.info()

data3 = data2.rename(columns={'EXTRA': 'y', 'PLT': 'plts'})

data3.head()

data3=data3[['ds','y','plts']]

data3=data3.dropna()

data3

data3=data3.loc[data3['y']>=0] 

data3

from prophet import Prophet

def get_test(x):

    #for x in range(0,3):

    data4=data3.loc[data3['plts']==x]

    m = Prophet()

    m.fit(data4)

    cast_future = m.make_future_dataframe(periods=24000,freq='60min')  

    fcast = m.predict(cast_future)

    return fcast

list_olts =data3.olts.unique()

for x in range(0,3):

    y=list_olts[x]

    fc=get_test(y)

    fc=fc[['ds', 'yhat', 'yhat_lower', 'yhat_upper']]

    fc['PLT_NAME']=y    

    fc.to_csv('./Predicted_DATA/'+y+'.csv')