# coding: utf-8

# In[2]:


# for basic operations
import numpy as np 
import pandas as pd 

# data visualization
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('fivethirtyeight')

data = pd.read_csv('C:/Users/Kai/Desktop/Assignment3/data/breast-w_preproccess.csv')


# In[3]:


# for basic operations
import numpy as np 
import pandas as pd 

# data visualization
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('fivethirtyeight')

data = pd.read_csv('C:/Users/Kai/Desktop/Assignment3/data/breast-w_preproccess.csv')

# printing the shape
print(data.shape)

# printing the head of the data
data.head()

# describing the data
data.describe()

# getting the info of the data
data.info()

# checking if the dataset contains any NULL Values
data.isnull().sum().sum()

sns.pairplot(data)


# In[14]:


# boxen plots

plt.rcParams['figure.figsize'] = (18, 16)

plt.subplot(2, 2, 1)
sns.boxplot(x = data['Class'], y = data['Cell_Size_Uniformity'], data = data, palette = 'rainbow')
plt.title('Class vs Cell_Size_Uniformity', fontsize = 20)

plt.subplot(2, 2, 2)
sns.boxplot(x = 'Class', y = 'Cell_Shape_Uniformity', data = data, palette = 'summer')
plt.title('Class vs Cell_Shape_Uniformity', fontsize = 20)

plt.subplot(2, 2, 3)
sns.boxplot(x = 'Class', y = 'Marginal_Adhesion', data = data, palette = 'spring')
plt.title('Class vs Marginal_Adhesion', fontsize = 20)

plt.subplot(2, 2, 4)
sns.boxplot(x = 'Class', y = 'Single_Epi_Cell_Size', data = data, palette = 'deep')
plt.title('Class vs Single_Epi_Cell_Size', fontsize = 20)

plt.show()



# In[16]:


plt.rcParams['figure.figsize'] = (18, 16)


plt.subplot(2, 2, 1)
sns.violinplot(x = data['Class'], y = data['Bare_Nuclei'], data = data, palette = 'rainbow')
plt.title('Class vs Bare_Nuclei', fontsize = 20)

plt.subplot(2, 2, 2)
sns.violinplot(x = 'Class', y = 'Bland_Chromatin', data = data, palette = 'summer')
plt.title('Class vs Bland_Chromatin', fontsize = 20)

plt.subplot(2, 2, 3)
sns.violinplot(x = 'Class', y = 'Normal_Nucleoli', data = data, palette = 'spring')
plt.title('Class vs Normal_Nucleoli', fontsize = 20)

plt.subplot(2, 2, 4)
sns.violinplot(x = 'Class', y = 'Mitoses', data = data, palette = 'deep')
plt.title('Class vs Mitoses', fontsize = 20)

plt.show()


# In[20]:


# boxen plots

plt.rcParams['figure.figsize'] = (18, 16)

plt.subplot(2, 2, 1)
sns.swarmplot(x = 'Class', y = 'Marginal_Adhesion', data = data, palette = 'spring')
plt.title('Class vs Marginal_Adhesion', fontsize = 20)

plt.subplot(2, 2, 2)
sns.stripplot(x = 'Class', y = 'Marginal_Adhesion', data = data, palette = 'spring')
plt.title('Class vs Marginal_Adhesion', fontsize = 20)

plt.subplot(2, 2, 3)
sns.barplot(x = 'Class', y = 'Marginal_Adhesion', data = data, palette = 'spring')
plt.title('Class vs Marginal_Adhesion', fontsize = 20)

plt.subplot(2, 2, 4)
sns.boxplot(x = 'Class', y = 'Marginal_Adhesion', data = data, palette = 'spring')
plt.title('Class vs Marginal_Adhesion', fontsize = 20)

plt.show()


# In[21]:


f, ax = plt.subplots(figsize=(18, 15))
corr = data.corr()
sns.heatmap(corr, mask=np.zeros_like(corr, dtype=np.bool), cmap=sns.diverging_palette(220, 10, as_cmap=True),
            square=True, ax=ax)


# In[22]:


#  DATA PREPROCESSING

# label encoding of the dependent variable

# importing label encoder
from sklearn.preprocessing import LabelEncoder

# performing label encoding
le = LabelEncoder()
data['Class'] = le.fit_transform(data['Class'])


# In[23]:


data['Class'].value_counts()


# In[65]:


# splitting the dependent and independent variables from the dataset

x = data.iloc[:,0:9]
y = data.iloc[:,9]

print(x.shape)
print(y.shape)


# In[70]:


from sklearn.model_selection import train_test_split

x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.25, random_state = 16)

print(x_train.shape)
print(y_train.shape)
print(x_test.shape)
print(y_test.shape)


# In[71]:


# standard scaling 

from sklearn.preprocessing import StandardScaler

sc = StandardScaler()

x_train = sc.fit_transform(x_train)
x_test = sc.fit_transform(x_test)


# In[90]:


# LOGISTIC REGRESSION

from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.model_selection import GridSearchCV, cross_val_score

# creating the model

model = DecisionTreeClassifier()

# feeding the training data into the model
model.fit(x_train, y_train)
y_prob = model.predict_proba(x_test)[:,1] # This will give you positive class prediction probabilities  
# print(y_prob)
y_pred = model.predict(x_test)

# Calculating the accuracies
print("Training accuracy :", model.score(x_train, y_train))
print("Testing accuarcy :", model.score(x_test, y_test))

# classification report
cr = classification_report(y_test, y_pred)
print(cr)

# confusion matrix 
plt.rcParams['figure.figsize'] = (5, 5)
cm = confusion_matrix(y_test, y_pred)
sns.heatmap(cm, annot = True, cmap = 'cool')


# In[91]:


features = data.columns
importance = model.feature_importances_
indices = np.argsort(importance)

plt.rcParams['figure.figsize'] = (15, 15)
plt.barh(range(len(indices)), importance[indices])
plt.yticks(range(len(indices)), features[indices])
plt.title('Feature Importance for Decision Tree', fontsize = 30)
plt.grid()
plt.tight_layout()
plt.show()


# In[92]:


from sklearn import metrics
auc_roc=metrics.roc_auc_score(y_test,y_pred)
print("auc_roc: ")
print(auc_roc)

from sklearn.metrics import roc_curve, auc
false_positive_rate, true_positive_rate, thresholds = roc_curve(y_test, y_prob)
roc_auc = auc(false_positive_rate, true_positive_rate)
print("roc_auc: ")
print(roc_auc)

import matplotlib.pyplot as plt
plt.figure(figsize=(10,10))
plt.title('ROC curve_Decision Tree')
plt.plot(false_positive_rate,true_positive_rate, color='red',label = 'AUC = %0.2f' % roc_auc)
plt.legend(loc = 'lower right')
plt.plot([0, 1], [0, 1],linestyle='--')
plt.axis('tight')
plt.ylabel('True Positive Rate')
plt.xlabel('False Positive Rate')


# In[96]:


# Support Vector Machine

from sklearn.svm import SVC

# creating a model
model = SVC()

# feeding the training data into the model
model.fit(x_train, y_train)

# y_prob = model.predict_proba(x_test)[:,1] # This will give you positive class prediction probabilities  

# predicting the test set results
y_pred = model.predict(x_test)

# Calculating the accuracies
print("Training accuracy :", model.score(x_train, y_train))
print("Testing accuarcy :", model.score(x_test, y_test))

# classification report
cr = classification_report(y_test, y_pred)
print(cr)

# confusion matrix 
plt.rcParams['figure.figsize'] = (5, 5)
cm = confusion_matrix(y_test, y_pred)
sns.heatmap(cm, annot = True, cmap = 'rainbow')


# In[94]:


from sklearn import metrics
auc_roc=metrics.roc_auc_score(y_test,y_pred)
print("auc_roc: ")
print(auc_roc)

from sklearn.metrics import roc_curve, auc
false_positive_rate, true_positive_rate, thresholds = roc_curve(y_test, y_prob)
roc_auc = auc(false_positive_rate, true_positive_rate)
print("roc_auc: ")
print(roc_auc)

import matplotlib.pyplot as plt
plt.figure(figsize=(10,10))
plt.title('ROC curve_Decision Tree')
plt.plot(false_positive_rate,true_positive_rate, color='red',label = 'AUC = %0.2f' % roc_auc)
plt.legend(loc = 'lower right')
plt.plot([0, 1], [0, 1],linestyle='--')
plt.axis('tight')
plt.ylabel('True Positive Rate')
plt.xlabel('False Positive Rate')