import pandas as pd
import numpy as np

df_train = pd.read_csv(r'C:UserspiushDesktopDatasetTitanictrain.csv')

df_train.head(2)

df_train.info()

df_train = df_train.drop(['Cabin','Name'],axis = 1)

age_median = df_train['Age'].median()
df_train['Age'] = df_train['Age'].fillna(age_median)

mode_embarked = df_train['Embarked'].mode().values[0]
df_train['Embarked'] = df_train['Embarked'].fillna(mode_embarked)

df_train['Gender'] = df_train['Sex'].map({'female': 0, 'male': 1}).astype(int)
df_train['Port'] = df_train['Embarked'].map({'C':1, 'S':2, 'Q':3}).astype(int)

df_train = df_train.drop(['Sex', 'Embarked'], axis=1)

cols = df_train.columns.tolist()
cols = [cols[1]] + cols[0:1] + cols[2:]
df_train = df_train[cols]

df_train['Ticket'] = pd.get_dummies(df_train['Ticket'],prefix ='Ticket')

df_train.info()

#print summary of data frame
summary = df_train.describe()
print(summary)

df_train.columns

import matplotlib.pyplot as plot
%matplotlib inline

for i in range(891):
    #assign color based on survived based on "0" or "1" labels
    if df_train.iat[i,8] == 1:
        pcolor = "red"
    else:
        pcolor = "blue"

    #plot rows of data as if they were series data
    dataRow = df_train.iloc[i,0:8]
    dataRow.plot(color=pcolor, alpha=0.5)

plot.xlabel("Attribute Index")
plot.ylabel(("Attribute Values"))
plot.show()

target = df_train['Survived']
#plot rows of data as if they were series data
dataRow = df_train.iloc[0:891,6]
plot.scatter(dataRow, target,alpha=0.5, s=120)

plot.xlabel("Attribute Value")
plot.ylabel("Target Value")
plot.show()

#calculate correlations between attributes

corMat = pd.DataFrame(df_train.corr())

#visualize correlations using heatmap
plot.pcolor(corMat)
plot.show()

labels = df_train['Survived'].values
df_train1 = df_train[['PassengerId','Pclass','Age', 'SibSp', 'Parch', 'Ticket', 'Fare','Gender', 'Port']]
xList = df_train1.values

from sklearn.ensemble import RandomForestClassifier

clf = RandomForestClassifier(n_estimators=100)

# Training data features, skip the first column 'Survived'
train_features = xList[:, 1:]

# 'Survived' column values
train_target = labels

# Fit the model to our training data
clf = clf.fit(train_features, train_target)
score = clf.score(train_features, train_target)
"Mean accuracy of Random Forest: {0}".format(score)

#import random
from sklearn import datasets, linear_model

def confusionMatrix(predicted, actual, threshold):
    if len(predicted) != len(actual): return -1
    tp = 0.0
    fp = 0.0
    tn = 0.0
    fn = 0.0
    for i in range(len(actual)):
        if actual[i] > 0.5: #labels that are 1.0  (positive examples)
            if predicted[i] > threshold:
                tp += 1.0 #correctly predicted positive
            else:
                fn += 1.0 #incorrectly predicted negative
        else:              #labels that are 0.0 (negative examples)
            if predicted[i] < threshold:
                tn += 1.0 #correctly predicted negative
            else:
                fp += 1.0 #incorrectly predicted positive
    rtn = [tp, fn, fp, tn]
    return rtn

#divide attribute matrix and label vector into training(2/3 of data) and test sets (1/3 of data)
indices = range(len(xList))
xListTest = [xList[i] for i in indices if i%3 == 0 ]
xListTrain = [xList[i] for i in indices if i%3 != 0 ]
labelsTest = [labels[i] for i in indices if i%3 == 0]
labelsTrain = [labels[i] for i in indices if i%3 != 0]

#form list of list input into numpy arrays to match input class for scikit-learn linear model
xTrain = np.array(xListTrain); yTrain = np.array(labelsTrain); xTest = np.array(xListTest); yTest = np.array(labelsTest)

#check shapes to see what they look like
print("Shape of xTrain array", xTrain.shape)
print("Shape of yTrain array", yTrain.shape)
print("Shape of xTest array", xTest.shape)
print("Shape of yTest array", yTest.shape)

#train linear regression model
dfModel = linear_model.LinearRegression()
dfModel.fit(xTrain,yTrain)

#generate predictions on in-sample error
trainingPredictions = dfModel.predict(xTrain)
print("Some values predicted by model", trainingPredictions[0:5], trainingPredictions[-6:-1])

#generate confusion matrix for predictions on training set (in-sample
confusionMatTrain = confusionMatrix(trainingPredictions, yTrain, 0.50)
#pick threshold value and generate confusion matrix entries
tp = confusionMatTrain[0]; fn = confusionMatTrain[1]; fp = confusionMatTrain[2]; tn = confusionMatTrain[3]

print("tp = " + str(tp) + "tfn = " + str(fn) + "n" + "fp = " + str(fp) + "ttn = " + str(tn) + 'n')

#Misclassification error rate 
def errorRate(tp,fn,fp,tn):
    #Accuracy
    acc = (tp + tn) / (tp + fn + fp + tn)
    return 1- acc

print("Miscalssification error rate is : " + str(errorRate(tp,fn,fp,tn)))

#generate predictions on out-of-sample data
testPredictions = dfModel.predict(xTest)

#generate confusion matrix from predictions on out-of-sample data
conMatTest = confusionMatrix(testPredictions, yTest, 0.25)
#pick threshold value and generate confusion matrix entries
tp = conMatTest[0]; fn = conMatTest[1]; fp = conMatTest[2]; tn = conMatTest[3]
print("tp = " + str(tp) + "tfn = " + str(fn) + "n" + "fp = " + str(fp) + "ttn = " + str(tn) + 'n')

print("Miscalssification error rate is : " + str(errorRate(tp,fn,fp,tn)))

from sklearn.metrics import roc_curve, auc
#generate ROC curve for in-sample

fpr, tpr, thresholds = roc_curve(yTrain,trainingPredictions)
roc_auc = auc(fpr, tpr)
print( 'AUC for in-sample ROC curve: %f' % roc_auc)

from sklearn.cross_validation import train_test_split
from sklearn import ensemble
from sklearn.metrics import roc_auc_score

#number of rows and columns in x matrix
nrows = len(xList)
ncols = len(xList[1])

#form x and y into numpy arrays and make up column names
X = np.array(xList)
y = np.array(labels)
dfNames = np.array(['V' + str(i) for i in range(ncols)])

#break into training and test sets.
xTrain, xTest, yTrain, yTest = train_test_split(X, y, test_size=0.30, random_state=531)

auc = []

nTreeList = range(50, 2000, 50)
for iTrees in nTreeList:
    depth = None
    maxFeat  = 8 #try tweaking
    dfRFModel = ensemble.RandomForestClassifier(n_estimators=iTrees, max_depth=depth, max_features=maxFeat,
                                                 oob_score=False, random_state=531)

    dfRFModel.fit(xTrain,yTrain)

    #Accumulate auc on test set
    prediction = dfRFModel.predict_proba(xTest)
    aucCalc = roc_auc_score(yTest, prediction[:,1:2])
    auc.append(aucCalc)

print("AUC" )
print(auc[-1])

#plot training and test errors vs number of trees in ensemble
plot.plot(nTreeList, auc)
plot.xlabel('Number of Trees in Ensemble')
plot.ylabel('Area Under ROC Curve - AUC')
#plot.ylim([0.0, 1.1*max(mseOob)])
plot.show()

df_train1.columns

# Plot feature importance
featureImportance = dfRFModel.feature_importances_
# normalize by max importance
featureImportance = featureImportance / featureImportance.max()

#plot importance of 8
idxSorted = np.argsort(featureImportance)[0:9]
idxTemp = np.argsort(featureImportance)[::-1]
print(idxTemp)
barPos = np.arange(idxSorted.shape[0]) + .5
plot.barh(barPos, featureImportance[idxSorted], align='center')
plot.yticks(barPos, dfNames[idxSorted])
plot.xlabel('Variable Importance')
plot.show()

#plot best version of ROC curve
fpr, tpr, thresh = roc_curve(yTest, list(prediction[:,1:2]))
ctClass = [i*0.01 for i in range(101)]

plot.plot(fpr, tpr, linewidth=2)
plot.plot(ctClass, ctClass, linestyle=':')
plot.xlabel('False Positive Rate')
plot.ylabel('True Positive Rate')
plot.show()

#form x and y into numpy arrays and make up column names
X = np.array(df_train1)
y = np.array(labels)
dfNames = np.array(['V' + str(i) for i in range(ncols)])

#break into training and test sets.
xTrain, xTest, yTrain, yTest = train_test_split(X, y, test_size=0.30, random_state=531)
#instantiate model
nEst = 2000
depth = 3
learnRate = 0.007
maxFeatures = 8
dfGBMModel = ensemble.GradientBoostingClassifier(n_estimators=nEst, max_depth=depth,
                                                         learning_rate=learnRate,
                                                         max_features=maxFeatures)
#train
dfGBMModel.fit(xTrain, yTrain)

# compute auc on test set as function of ensemble size
auc = []
aucBest = 0.0
predictions = dfGBMModel.staged_decision_function(xTest)
for p in predictions:
    aucCalc = roc_auc_score(yTest, p)
    auc.append(aucCalc)

    #capture best predictions
    if aucCalc > aucBest:
        aucBest = aucCalc
        pBest = p

idxBest = auc.index(max(auc))

#print best values
print("Best AUC" )
print(auc[idxBest])
print("Number of Trees for Best AUC")
print(idxBest)

df_test = pd.read_csv(r'C:UserspiushDesktopDatasetTitanictest.csv')

df_test.head(2)

df_test.info()

df_test = df_test.drop(['Cabin','Name'],axis = 1)

age_median = df_test['Age'].median()
df_test['Age'] = df_test['Age'].fillna(age_median)

mean_fare = df_test['Fare'].mean()
df_test['Fare'] = df_test['Fare'].fillna(mean_fare)

df_test['Gender'] = df_test['Sex'].map({'female': 0, 'male': 1}).astype(int)
df_test['Port'] = df_test['Embarked'].map({'C':1, 'S':2, 'Q':3}).astype(int)

df_test = df_test.drop(['Sex', 'Embarked'], axis=1)

cols = df_test.columns.tolist()
cols = [cols[1]] + cols[0:1] + cols[2:]
df_test = df_test[cols]

df_test['Ticket'] = pd.get_dummies(df_test['Ticket'],prefix ='Ticket')

df_test.info()

# Data wrangle the test set and convert it to a numpy array
test_data = df_test.values

# Get the test data features, skipping the first column 'PassengerId'
test_x = test_data[:,]

# Predict the Survival values for the test data
test_y =list(map(int, dfRFModel.predict(test_x)))

df_test['Survived'] = test_y
df_test[['PassengerId', 'Survived']]
    .to_csv('results-3rf.csv', index=False)