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# import common APIs
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
from mpl_toolkits.mplot3d import Axes3D
import seaborn as sns
import os
from sklearn.preprocessing import MinMaxScaler, StandardScaler, LabelEncoder, OneHotEncoder
from sklearn.decomposition import PCA
from sklearn import cross_validation, naive_bayes, tree, svm, ensemble
from sklearn.metrics import classification_report,confusion_matrix,precision_recall_curve,auc,roc_auc_score,roc_curve
from xgboost import XGBClassifier
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# Data observation
filepath = '/Users/mac/Desktop/Kaggle_datasets/Indian_LiverDz/'
filename01 = 'indian_liver_patient.csv'
df = pd.read_csv(os.path.join(filepath, filename01))
df.head()
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df.info()
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df[df.Albumin_and_Globulin_Ratio.isnull()]
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df['Albumin_and_Globulin_Ratio'].fillna(df.Albumin_and_Globulin_Ratio.median(),inplace=True)
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dict_Gender = {'Male':0,'Female':1}
df['Gender'] = df['Gender'].map(dict_Gender)
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df['Dataset'] = np.abs(df['Dataset']-2)
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df.head()
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sns.countplot(x='Dataset', data=df); #0沒病，1有病
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sns.barplot(x="Gender", y="Dataset", data=df); #0:male, 1:female
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sns.barplot(x="Gender", y="Albumin_and_Globulin_Ratio", hue='Dataset', data=df); #0:male, 1:female
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sns.barplot(x="Gender", y="Alamine_Aminotransferase", hue='Dataset', data=df); #0:male, 1:female
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sns.barplot(x="Gender", y="Total_Bilirubin", hue='Dataset', data=df); #0:male, 1:female
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sns.jointplot(x=df.Alamine_Aminotransferase, y=df.Aspartate_Aminotransferase, );
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D0 = df.loc[df.Dataset == 0]
D1 = df.loc[df.Dataset == 1]
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ax = sns.jointplot(D0.Alamine_Aminotransferase, D0.Aspartate_Aminotransferase, color="Red", size=5)
ax = sns.jointplot(D1.Alamine_Aminotransferase, D1.Aspartate_Aminotransferase, color="Blue", size=5)
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g = sns.FacetGrid(df, hue="Dataset", palette="Set1", size=7, hue_kws={"marker": ["o", "x"]})
g.map(plt.scatter, "Alamine_Aminotransferase", "Aspartate_Aminotransferase",
s=100, linewidth=.5, edgecolor="white",alpha=1)
g.add_legend();
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g = sns.FacetGrid(df, hue="Dataset", palette="Set1", size=7, hue_kws={"marker": ["o", "x"]})
g.map(plt.scatter, "Total_Bilirubin", "Direct_Bilirubin",
s=100, linewidth=.5, edgecolor="white",alpha=1)
g.add_legend();
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g = sns.FacetGrid(df, hue="Dataset", palette="Set1", size=7, hue_kws={"marker": ["^", "v"]})
g.map(plt.scatter, "Total_Protiens", "Albumin",
s=100, linewidth=.5, edgecolor="white",alpha=1)
g.add_legend();
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g = sns.FacetGrid(df, col="Dataset", row="Gender", size=3)
g = g.map(plt.scatter, 'Alamine_Aminotransferase', 'Aspartate_Aminotransferase', edgecolor="w")
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g = sns.FacetGrid(df, col="Dataset", row="Gender", size=3)
g = g.map(plt.scatter, "Total_Bilirubin", "Direct_Bilirubin", edgecolor="w")
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# Corr heatmap：直接看一排相關係數高低
k = 10 #number of variables for heatmap
corrmat = df.corr()
cols = corrmat.nlargest(k, 'Dataset')['Dataset'].index
cm = np.corrcoef(df[cols].values.T)
plt.figure(figsize=(10,10)) #可以調整大小
sns.set(font_scale=1.25)
hm = sns.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 15},
yticklabels = cols.values, xticklabels = cols.values, cmap='rainbow')
#ax.xaxis.set_ticks_position('top') #特別用於confusion matrix好讀
plt.show()
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# Data preprocessing
from sklearn.utils import shuffle
shuffle_df = shuffle(df, random_state=42)
df_label = shuffle_df['Dataset']
df_feature = shuffle_df.drop('Dataset', axis=1)
cut_point = round(len(df)*0.6)
train_feature = np.array(df_feature.values[:cut_point,:])
train_label = np.array(df_label.values[:cut_point])
test_feature = np.array(df_feature.values[cut_point:,:])
test_label = np.array(df_label.values[cut_point:])
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### naive_bayes.BernoulliNB()
from sklearn import cross_validation, naive_bayes
X_train,X_test,y_train,y_test = cross_validation.train_test_split(train_feature,train_label,
test_size=0.25, random_state=0,stratify=train_label)
clf=naive_bayes.BernoulliNB()
clf.fit(X_train,y_train)
print("Traing Score:%f"%clf.score(train_feature,train_label))
print("Testing Score:%f"%clf.score(test_feature,test_label))
y_predict = clf.predict(X_test)
print('\n'+classification_report(y_test,y_predict))
y_predict2 = clf.predict(test_feature)
print('\n'+classification_report(test_label,y_predict2))
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### naive_bayes.GaussianNB()
from sklearn import cross_validation, naive_bayes
X_train,X_test,y_train,y_test = cross_validation.train_test_split(train_feature,train_label,
test_size=0.25, random_state=0,stratify=train_label)
clf=naive_bayes.GaussianNB()
clf.fit(X_train,y_train)
print("Traing Score:%f"%clf.score(train_feature,train_label))
print("Testing Score:%f"%clf.score(test_feature,test_label))
y_predict = clf.predict(X_test)
print('\n'+classification_report(y_test,y_predict))
y_predict2 = clf.predict(test_feature)
print('\n'+classification_report(test_label,y_predict2))
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### tree.DecisionTreeClassifier()
from sklearn import cross_validation,tree
X_train,X_test,y_train,y_test = cross_validation.train_test_split(train_feature,train_label,
test_size=0.25, random_state=0,stratify=train_label)
clf=tree.DecisionTreeClassifier()
clf.fit(X_train,y_train)
print("Traing Score:%f"%clf.score(train_feature,train_label))
print("Testing Score:%f"%clf.score(test_feature,test_label))
y_predict = clf.predict(X_test)
print('\n'+classification_report(y_test,y_predict))
y_predict2 = clf.predict(test_feature)
print('\n'+classification_report(test_label,y_predict2))
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# confusion matrix
prediction2 = clf.predict(test_feature)
prediction2_list = prediction2.reshape(-1).astype(int)
label2_list = test_label.astype(int)
print(classification_report(label2_list, prediction2_list))
print(confusion_matrix(label2_list, prediction2_list))
# conf heatmap
conf = confusion_matrix(label2_list, prediction2_list)
f, ax= plt.subplots(figsize = (5, 5))
sns.heatmap(conf, annot=True, ax=ax, fmt='d', annot_kws={'size':15}) #字體大小
ax.xaxis.set_ticks_position('top') #Making x label be on top is common in textbooks.
plt.show()
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# model_efficacy
def model_efficacy(conf):
total_num = np.sum(conf)
sen = conf[0][0]/(conf[0][0]+conf[1][0])
spe = conf[1][1]/(conf[1][0]+conf[1][1])
false_positive_rate = conf[0][1]/(conf[0][1]+conf[1][1])
false_negative_rate = conf[1][0]/(conf[0][0]+conf[1][0])
print('total_num: ',total_num)
print('G1P1: ',conf[0][0]) #G = gold standard; P = prediction
print('G0P1: ',conf[0][1])
print('G1P0: ',conf[1][0])
print('G0P0: ',conf[1][1])
print('##########################')
print('sensitivity: ',sen)
print('specificity: ',spe)
print('false_positive_rate: ',false_positive_rate)
print('false_negative_rate: ',false_negative_rate)
return total_num, sen, spe, false_positive_rate, false_negative_rate
conf = confusion_matrix(label2_list, prediction2_list)
model_efficacy(conf)
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# ROC curve
false_positive_rate, true_positive_rate, thresholds = roc_curve(label2_list, prediction2_list)
roc_auc = auc(false_positive_rate, true_positive_rate)
plt.title('Receiver Operating Characteristic')
plt.plot(false_positive_rate, true_positive_rate, 'b', label='AUC = %0.2f'% roc_auc)
plt.legend(loc='lower right')
plt.plot([0,1],[0,1],'r--')
plt.xlim([-0.1,1.2])
plt.ylim([-0.1,1.2])
plt.ylabel('True Positive Rate')
plt.xlabel('False Positive Rate')
plt.show()
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### svm.LinearSVC()
from sklearn import cross_validation,svm
X_train,X_test,y_train,y_test = cross_validation.train_test_split(train_feature,train_label,
test_size=0.25, random_state=0,stratify=train_label)
clf=svm.LinearSVC()
clf.fit(X_train,y_train)
print("Traing Score:%f"%clf.score(train_feature,train_label))
print("Testing Score:%f"%clf.score(test_feature,test_label))
y_predict = clf.predict(X_test)
print('\n'+classification_report(y_test,y_predict))
y_predict2 = clf.predict(test_feature)
print('\n'+classification_report(test_label,y_predict2))
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### ensemble.RandomForestClassifier()
from sklearn import cross_validation,ensemble
X_train,X_test,y_train,y_test = cross_validation.train_test_split(train_feature,train_label,
test_size=0.25, random_state=0,stratify=train_label) #分層取樣
clf=ensemble.RandomForestClassifier()
clf.fit(X_train,y_train)
print("Traing Score:%f"%clf.score(train_feature,train_label))
print("Testing Score:%f"%clf.score(test_feature,test_label))
y_predict = clf.predict(X_test)
print('\n'+classification_report(y_test,y_predict))
y_predict2 = clf.predict(test_feature)
print('\n'+classification_report(test_label,y_predict2))
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# XGBClassifier()
from xgboost import XGBClassifier
X_train,X_test,y_train,y_test = cross_validation.train_test_split(train_feature,train_label,
test_size=0.25, random_state=0,stratify=train_label) #分層取樣
clf=XGBClassifier()
clf.fit(X_train,y_train)
print("Traing Score:%f"%clf.score(train_feature,train_label))
print("Testing Score:%f"%clf.score(test_feature,test_label))
y_predict = clf.predict(X_test)
print('\n'+classification_report(y_test,y_predict))
y_predict2 = clf.predict(test_feature)
print('\n'+classification_report(test_label,y_predict2))
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