Bolajio - 1 year ago 87

Python Question

I have compiled my code for a polynomial graph, but it is not plotting. I am using SVR(support vector regression) from scikit learn and my code is below. It is not showing any error message, and it is just showing my data. I don't know what is going on. Does anyone? It is not even showing anything on the variable console describing my data.

`import pandas as pd`

import numpy as np

from sklearn.svm import SVR

from sklearn import cross_validation

from sklearn.linear_model import LinearRegression

import matplotlib.pyplot as plt

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

print(df)

df = df[['Date','Amount_prod','Beverage_index']]

x = np.array(df.Amount_prod)

y = np.array(df.Beverage_index)

x_train, x_test, y_train, y_test = cross_validation.train_test_split(

x, y, test_size=0.2)

x_train = np.pad(x, [(0,0)], mode='constant')

x_train.reshape((26,1))

y_train = np.pad(y, [(0,0)], mode='constant')

y_train.reshape((26,1))

x_train = np.arange(26).reshape((26, 1))

x_train = x.reshape((26, 1))

c = x.T

np.all(x_train == c)

x_test = np.arange(6).reshape((-1,1))

x_test = x.reshape((-1,1))

c2 = x.T

np.all(x_test == c2)

y_test = np.arange(6).reshape((-1,1))

y_test = y.reshape((-1,1))

c2 = y.T

np.all(y_test ==c2)

svr_poly = SVR(kernel='poly', C=1e3, degree=2)

y_poly = svr_poly.fit(x_train,y_train).predict(x_train)

plt.scatter(x_train, y_train, color='black')

plt.plot(x_train, y_poly)

plt.show()

Data sample:

`Date Amount_prod Beverage_index`

1990 83000 78

1991 102000 78

1992 94567 86

1993 101340 88

1994 96909 123

1995 92987 101

1996 103489 99

1997 99650 109

1998 107849 110

1999 123467 90

2000 112586 67

2001 113485 67

2002 108765 90

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Answer Source

Try the code below. Support Vector Machines expect their input to have zero mean and unit variance. It's not the plot, that's blocking. It's the call to `fit`

.

```
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
svr_poly = make_pipeline(StandardScaler(), SVR(kernel='poly', C=1e3, degree=2))
y_poly = svr_poly.fit(x_train,y_train).predict(x_train)
```

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