import quandl as Quandl, math
import numpy as np
import pandas as pd
from sklearn import preprocessing, model_selection as cross_validation, svm
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
from matplotlib import style
import datetime
import pickle
style.use('ggplot')
#Get function we did not pass any input
#get function gives output as data frame from Quandl
def get_data():
df = Quandl.get("WIKI/AMZN",api_key='tJbUksb_TeZXtwAgDeia')
return df
#df = get_data()
#Preprocess function takes input as df
#it clean the data and send output as cleaning data df_clean
def preprocess_data(df):
#taking Adj. Open, Adj. High, Adj. Low, Adj. Close, Adj. Volume from dataframe
df = df[['Adj. Open', 'Adj. High', 'Adj. Low', 'Adj. Close', 'Adj. Volume']]
#calculating the high low percentage
df['HL_PCT'] = (df['Adj. High'] - df['Adj. Low']) / df['Adj. Low'] * 100.0
#calculating high low percentage
df['PCT_change'] = (df['Adj. Close'] - df['Adj. Open']) / df['Adj. Open'] * 100.0
#adding HL_PCT, PCT_change to the dataframe
df = df[['Adj. Close', 'HL_PCT', 'PCT_change', 'Adj. Volume']]
df.fillna(value=-99999, inplace=True)
df.dropna(inplace=True)
return df
#df = preprocess_data(df)
#print(df)
#pred_split function takes cleaning data(df_clean) as input
#it will split data as prediction and testtrain data
#it gives output as X_pred,y_pred,X_testtrain,y_testtrain
def pred_split(df):
forecast_col = 'Adj. Close'
#calculating the forecast_out
forecast_out = int(math.ceil(0.01 * len(df)))
df['label'] = df[forecast_col].shift(-forecast_out)
X = np.array(df.drop(['label'], 1))
#assign the X_testtrain data
X_testtrain= X[:-forecast_out]
#assign the X_pred data
X_pred= X[-forecast_out:]
y = np.array(df['label'])
#assign the y_testtrain data
y_testtrain = y[:-forecast_out]
#assign the y_pred data
y_pred = y[-forecast_out:]
return X_pred,y_pred,X_testtrain,y_testtrain
#X_pred,y_pred,X_testtrain,y_testtrain = pred_split(df)
#print(X_pred,y_pred)
#print(X_testtrain[0],y_testtrain[0])
#split function takes X_testtrain,y_testtrain data as Input
#it gives output as X_train,X_test,y_train,y_test
def split(X_testtrain,y_testtrain):
X_train, X_test, y_train, y_test = cross_validation.train_test_split(X_testtrain, y_testtrain, test_size=0.2)
return X_train, X_test, y_train, y_test
#X_train, X_test, y_train, y_test = split(X_testtrain,y_testtrain)
#print(X_train[0])
#print(X_test[0])
#print(y_train[0])
#print(y_test[0])
#train function takes input as Taining data(X_train,y_train)
#it trains the model
#it gives model as output
def train(X_train,y_train):
model = LinearRegression(n_jobs=-1)
model.fit(X_train,y_train)
return model
#model = train(X_train,y_train)
#print(model)
#savemodel takes model as input
#it save the model in pickle file
def savemodel(model):
with open('linearregression.pickle','wb') as f:
pickle.dump(model, f)
#savemodel(model)
#test functions takes testing data(X_test,y_test)as input
#it gives confidence interval as output
def test(X_test,y_test):
confidence = model.score(X_test, y_test)
return confidence
#confidence = test(X_test,y_test)
#print('Confidence:', confidence)
#pred function takes input as prediction data(X_pred)
#it gives output as future predicted values
def predict(X_pred):
#find the future prediction values
y_predict = model.predict(X_pred)
return y_predict
#y_predict=predict(X_pred)
#print(y_predict)
#visualixzation function takes closing date and forecast values as input
#it gives out as graph
def visualize(date,price):
#Plot the graph for visualizayion
df['dates'] = np.nan
#Predict for every day after the last day in training set
#print(df.head(0))
last_date = df.iloc[-1].name
last_unix = last_date.timestamp()
one_day = 86400
next_unix = last_unix + one_day
for i in y_predict:
next_date = datetime.datetime.fromtimestamp(next_unix)
next_unix += 86400
df.loc[next_date] = [np.nan for _ in range(len(df.columns)-1)]+[i]
plt.plot(df[date])
plt.plot(df[price])
plt.legend(loc=4)
plt.xlabel('Date')
plt.ylabel('Price')
plt.show()
#visualize('dates','Adj. Close')
def main():
df = get_data()
df = preprocess_data(df)
X_pred,y_pred,X_testtrain,y_testtrain = pred_split(df)
X_train, X_test, y_train, y_test = split(X_testtrain,y_testtrain)
model = train(X_train,y_train)
savemodel(model)
confidence = test(X_test,y_test)
print('Confidence:', confidence)
y_predict=predict(X_pred)
visualize('dates','Adj. Close')
if __name__ == "__main__":
main()
i am getting NameError: name 'model' is not defined while running
please help out how to fix this error
