from sklearn.linear_model import LogisticRegression
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from pyts.datasets import load_gunpoint
from pyts.transformation import ShapeletTransform
from datetime import time
# Toy dataset
data=pd.read_csv('dataset11.csv')
pf=data.head(10)


y=data[['Q0']]
X=data[['TS']]



X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=10)

print(X_train)

# as columns.
dataframe = pd.DataFrame(
  pf,columns=['TS', 'Q0'])
 
# Changing the datatype of Date, from
# Object to datetime64
#dataframe["Sample2"] = Sample2.time.strptime("%T")
 
# Setting the Date as index
dataframe = dataframe.set_index("TS")
dataframe


 
# setting figure size to 12, 10
plt.figure(figsize=(12, 6))
 
# Labelling the axes and setting
# a title
plt.xlabel("Time")
plt.ylabel("Values")
plt.title("Vibration")

 
# plotting the "A" column alone
plt.plot(dataframe["Q0"])
plt.legend(loc='best', fontsize=8)
plt.show()

st = ShapeletTransform(window_sizes='auto', sort=True)
X_new = st.fit_transform(X_train, y_train)

print(X_new)

# Visualize the four most discriminative shapelets
plt.figure(figsize=(6, 4))
for i, index in enumerate(st.indices_[:4]):
    idx, start, end = index
    plt.plot(X_train[idx], color='C{}'.format(i),
             label='Sample {}'.format(idx))
    plt.plot(np.arange(start, end), X_train[idx, start:end],
             lw=5, color='C{}'.format(i))

plt.xlabel('Time', fontsize=12)
plt.title('The four most discriminative shapelets', fontsize=14)
plt.legend(loc='best', fontsize=8)
plt.show()