plotting.py

import pandas as pd
from sklearn.ensemble import RandomForestClassifier
#from sklearn.tree import DecisionTreeClassifier
import numpy as np
import sklearn.metrics as sk
from sklearn.metrics import classification_report
import itertools
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix
Ami_data = pd.read_csv("E:\\data\\v2\\dataset.csv",delimiter=',', encoding="utf-8-sig")

Ami_data['is_train'] = np.random.uniform(0, 1, len(Ami_data)) <= .75
features = Ami_data.columns[2:40]
features

x_Data_frame = Ami_data.iloc[:,2:40]
x_data = Ami_data[list(x_Data_frame)].values

y_Data_frame = Ami_data.iloc[:,40:43]
y_data = Ami_data[list(y_Data_frame)].values


Ami_data.head(10)

Ami_data['result'] = Ami_data[list(y_Data_frame)].idxmax(axis = 1)
Ami_data 

factorized_Value = pd.factorize(Ami_data['result'])[0]

train_Data = Ami_data[:22]
test_Data = Ami_data[22:]

y = pd.factorize(train_Data['result'])[0]

ran = RandomForestClassifier(n_estimators=200)
#ran = RandomForestClassifier()
rfc = ran.fit(train_Data[features],y.ravel())
scr = ran.score(test_Data[features],factorized_Value[22:].ravel())


y_pred = ran.predict(test_Data[features])

print('Random Forest Classifier\n__________________________________')

confusion_matrix = sk.confusion_matrix(factorized_Value[22:].ravel(), y_pred)
print('Confusion Matrix: \n', confusion_matrix)

print('Accuracy: ',scr)

target_names = ['Heart Attack', 'May be heart attack', 'No heart attack']
print(classification_report(factorized_Value[22:].ravel(), y_pred, target_names=target_names))

#the error begin from here. cant plot the matrix.
def plot_confusion_matrix(cm, classes,
                          normalize=False,
                          title='Confusion matrix',
                          cmap=plt.cm.Blues):
    """
    This function prints and plots the confusion matrix.
    Normalization can be applied by setting `normalize=True`.
    """
    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
        print("Normalized confusion matrix")
    else:
        print('Confusion matrix, without normalization')

    print(cm)
    plt.imshow(cm, interpolation='nearest', cmap=cmap)
    plt.title(title)
    plt.colorbar()
    tick_marks = np.arange(len(classes))
    plt.xticks(tick_marks, classes, rotation=45)
    plt.yticks(tick_marks, classes)

    fmt = '.2f' if normalize else 'd'
    thresh = cm.max() / 2.
    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
        plt.text(j, i, format(cm[i, j], fmt),
                 horizontalalignment="center",
                 color="white" if cm[i, j] > thresh else "black")

    plt.tight_layout()
    plt.ylabel('True label')
    plt.xlabel('Predicted label')

# Compute confusion matrix
cnf_matrix = confusion_matrix(factorized_Value[22:].ravel(), y_pred)
np.set_printoptions(precision=2)

# Plot non-normalized confusion matrix
plt.figure()
plot_confusion_matrix(cnf_matrix, classes=class_names,
                      title='Confusion matrix, without normalization')

# Plot normalized confusion matrix
fig=plt.figure()
plot_confusion_matrix(cnf_matrix, classes=class_names, normalize=True,
                      title='Normalized confusion matrix')

plt.show()
fig.savefig('E:/plot.png')