# Import libraries
import numpy as np
import pandas as pd
from time import time
from sklearn.metrics import f1_score

# Read student data
student_data = pd.read_csv("student-data.csv")
print "Student data read successfully!"


"""
Explanation: Machine Learning Engineer Nanodegree
Supervised Learning
Project 2: Building a Student Intervention System
Welcome to the second project of the Machine Learning Engineer Nanodegree! In this notebook, some template code has already been provided for you, and it will be your job to implement the additional functionality necessary to successfully complete this project. Sections that begin with 'Implementation' in the header indicate that the following block of code will require additional functionality which you must provide. Instructions will be provided for each section and the specifics of the implementation are marked in the code block with a 'TODO' statement. Please be sure to read the instructions carefully!
In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a 'Question X' header. Carefully read each question and provide thorough answers in the following text boxes that begin with 'Answer:'. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide.  

Note: Code and Markdown cells can be executed using the Shift + Enter keyboard shortcut. In addition, Markdown cells can be edited by typically double-clicking the cell to enter edit mode.

Question 1 - Classification vs. Regression
Your goal for this project is to identify students who might need early intervention before they fail to graduate. Which type of supervised learning problem is this, classification or regression? Why?
Answer:  It's a classification problem since students are basicaly catogorized to "need early intervention" or "doesn't need early intervention" subgroups
Exploring the Data
Run the code cell below to load necessary Python libraries and load the student data. Note that the last column from this dataset, 'passed', will be our target label (whether the student graduated or didn't graduate). All other columns are features about each student.
End of explanation
"""


# TODO: Calculate number of students
n_students = len(student_data)

# TODO: Calculate number of features
n_features = len(student_data.columns)-1

# TODO: Calculate passing students
n_passed = student_data['passed'].value_counts()['yes']

# TODO: Calculate failing students
n_failed = student_data['passed'].value_counts()['no']

# TODO: Calculate graduation rate
grad_rate = float(n_passed)*100/n_students

# Print the results
print "Total number of students: {}".format(n_students)
print "Number of features: {}".format(n_features)
print "Number of students who passed: {}".format(n_passed)
print "Number of students who failed: {}".format(n_failed)
print "Graduation rate of the class: {:.2f}%".format(grad_rate)

"""
Explanation: Implementation: Data Exploration
Let's begin by investigating the dataset to determine how many students we have information on, and learn about the graduation rate among these students. In the code cell below, you will need to compute the following:
- The total number of students, n_students.
- The total number of features for each student, n_features.
- The number of those students who passed, n_passed.
- The number of those students who failed, n_failed.
- The graduation rate of the class, grad_rate, in percent (%).
End of explanation
"""


# Extract feature columns
feature_cols = list(student_data.columns[:-1])

# Extract target column 'passed'
target_col = student_data.columns[-1] 

# Show the list of columns
print "Feature columns:\n{}".format(feature_cols)
print "\nTarget column: {}".format(target_col)

# Separate the data into feature data and target data (X_all and y_all, respectively)
X_all = student_data[feature_cols]
y_all = student_data[target_col]

# Show the feature information by printing the first five rows
print "\nFeature values:"
print X_all.head()

"""
Explanation: Preparing the Data
In this section, we will prepare the data for modeling, training and testing.
Identify feature and target columns
It is often the case that the data you obtain contains non-numeric features. This can be a problem, as most machine learning algorithms expect numeric data to perform computations with.
Run the code cell below to separate the student data into feature and target columns to see if any features are non-numeric.
End of explanation
"""


def preprocess_features(X):
    ''' Preprocesses the student data and converts non-numeric binary variables into
        binary (0/1) variables. Converts categorical variables into dummy variables. '''
    
    # Initialize new output DataFrame
    output = pd.DataFrame(index = X.index)

    # Investigate each feature column for the data
    for col, col_data in X.iteritems():
        # If data type is non-numeric, replace all yes/no values with 1/0
        if col_data.dtype == object:
            col_data = col_data.replace(['yes', 'no'], [1, 0])

        # If data type is categorical, convert to dummy variables
        if col_data.dtype == object:
            # Example: 'school' => 'school_GP' and 'school_MS'
            col_data = pd.get_dummies(col_data, prefix = col)  
        
        # Collect the revised columns
        output = output.join(col_data)
    
    return output

X_all = preprocess_features(X_all)
print "Processed feature columns ({} total features):\n{}".format(len(X_all.columns), list(X_all.columns))

"""
Explanation: Preprocess Feature Columns
As you can see, there are several non-numeric columns that need to be converted! Many of them are simply yes/no, e.g. internet. These can be reasonably converted into 1/0 (binary) values.
Other columns, like Mjob and Fjob, have more than two values, and are known as categorical variables. The recommended way to handle such a column is to create as many columns as possible values (e.g. Fjob_teacher, Fjob_other, Fjob_services, etc.), and assign a 1 to one of them and 0 to all others.
These generated columns are sometimes called dummy variables, and we will use the pandas.get_dummies() function to perform this transformation. Run the code cell below to perform the preprocessing routine discussed in this section.
End of explanation
"""


# TODO: Import any additional functionality you may need here
from sklearn import cross_validation

# TODO: Set the number of training points
num_train = 300

# Set the number of testing points
num_test = X_all.shape[0] - num_train

# TODO: Shuffle and split the dataset into the number of training and testing points above

X_train, X_test, y_train, y_test = cross_validation.train_test_split(X_all, y_all, stratify = y_all, test_size= 95, 
            random_state = 42 )
# Show the results of the split
print "Training set has {} samples.".format(X_train.shape[0])
print "Testing set has {} samples.".format(X_test.shape[0])

"""
Explanation: Implementation: Training and Testing Data Split
So far, we have converted all categorical features into numeric values. For the next step, we split the data (both features and corresponding labels) into training and test sets. In the following code cell below, you will need to implement the following:
- Randomly shuffle and split the data (X_all, y_all) into training and testing subsets.
  - Use 300 training points (approximately 75%) and 95 testing points (approximately 25%).
  - Set a random_state for the function(s) you use, if provided.
  - Store the results in X_train, X_test, y_train, and y_test.
End of explanation
"""


def train_classifier(clf, X_train, y_train):
    ''' Fits a classifier to the training data. '''
    
    # Start the clock, train the classifier, then stop the clock
    start = time()
    clf.fit(X_train, y_train)
    end = time()
    
    # Print the results
    print "Trained model in {:.4f} seconds".format(end - start)


    
def predict_labels(clf, features, target):
    ''' Makes predictions using a fit classifier based on F1 score. '''
    
    # Start the clock, make predictions, then stop the clock
    start = time()
    y_pred = clf.predict(features)
    end = time()
    
    # Print and return results
    print "Made predictions in {:.4f} seconds.".format(end - start)
    return f1_score(target.values, y_pred, pos_label='yes')


def train_predict(clf, X_train, y_train, X_test, y_test):
    ''' Train and predict using a classifer based on F1 score. '''
    
    # Indicate the classifier and the training set size
    print "Training a {} using a training set size of {}. . .".format(clf.__class__.__name__, len(X_train))
    
    # Train the classifier
    train_classifier(clf, X_train, y_train)
    
    # Print the results of prediction for both training and testing
    print "F1 score for training set: {:.4f}.".format(predict_labels(clf, X_train, y_train))
    print "F1 score for test set: {:.4f}.".format(predict_labels(clf, X_test, y_test))


"""
Explanation: Training and Evaluating Models
In this section, you will choose 3 supervised learning models that are appropriate for this problem and available in scikit-learn. You will first discuss the reasoning behind choosing these three models by considering what you know about the data and each model's strengths and weaknesses. You will then fit the model to varying sizes of training data (100 data points, 200 data points, and 300 data points) and measure the F<sub>1</sub> score. You will need to produce three tables (one for each model) that shows the training set size, training time, prediction time, F<sub>1</sub> score on the training set, and F<sub>1</sub> score on the testing set.
Question 2 - Model Application
List three supervised learning models that are appropriate for this problem. What are the general applications of each model? What are their strengths and weaknesses? Given what you know about the data, why did you choose these models to be applied?
Answer: 
 Why following models?" 
Since our problem is a supervised classification problem I have chosen these three models which relatively work good on this kind of problems 
 Decission Tree
There are many application of Decision Trees algorithm in industry for example "Project Risk Management" softwares which are built by many companies like Salesforce are using this algorithm as part of their package for SWOT (Strengths, Weaknesses, Oppportunities, and Threats) Analysis. In general when there are some  path of connected rulles or conditions (connected nodes and branches) which at the end they lead in a conclusion (Target Value) it is good to consider decision trees.  
Pros:

Decision trees are easy to interpret and even regular user is able to understand the folow if it has reasonable number of nodes. 
Both nominal and numerical inputs are accepted.
Decision trees can handel datasets that may have errors.
They can handel datasets that have missing values.

Cons:

Most decision tree algorithms require that the target have discrete values.
Since decision trees use the “divide and conquer” method they work poorly if many complex interactions are present.
Decision trees are susceptible for overfitting.

Reason to choose:
Because Decision Tree is easy to interpret and explain to non-professional users I tried this algorithm first.
 SVM 
SVM is a supervised machine learning algorithm which can be used for classification or regression problems. It uses a technique called the kernel trick to transform your data and then based on these transformations it finds an optimal boundary between the possible outputsas. Svm has a significant discriminative power for classification, especially in cases where sample sizes are small and a large number of variables are involved (high-dimensionality space). support vector machine algorithm, has demonstrated high performance in solving classification problems in many biomedical fields, especially in bioinformatics   
Pros:

Finds the optimal seperation hyperplane
Can work with high dimentional data

Cons:

Needs both negative and positive examples
Best Kernel should be chosen
Needs a lot of CPU and Memory time

Reason to choose:
Since there are relatively high number of features compared to data records and the fact that SVM works well with high dimentional data so I tried this algorithm too.
AdaBoost
AdaBoost or adaptive boosting is a ML meta-algorithem which basicaly tries to improve other algorithems performance. So Adaboost fit the data with a chosen algorithem which by default is Decision tree and repeats this process over and over again but in each round it diagnose the hard examples which are missclassified and put more focus on theme by changing theire weight. in general if train/evaluation time is not an issue and higher accuracy is desired in most cases adaboost outperforms to other algorithms however if time matters usually boosting approach is not an ideal choice. One of the first and succesfull application of this algorithm was 'optical character recognition'(OCR) problem on digital handwritten digits.     
Pros:

Overall better perfomance and accuracy compared to non-boosting models
Finds weak learners and combine diffrent classifiers

Cons:

Has more complexity and is time consuming
Not ideal for models which requires instant result

Reason to choose:
I got a better solution with SVM previously but since higher accuracy might be more important than the speed in this case I tried Adaboost to compare with the SVM.
Setup
Run the code cell below to initialize three helper functions which you can use for training and testing the three supervised learning models you've chosen above. The functions are as follows:
- train_classifier - takes as input a classifier and training data and fits the classifier to the data.
- predict_labels - takes as input a fit classifier, features, and a target labeling and makes predictions using the F<sub>1</sub> score.
- train_predict - takes as input a classifier, and the training and testing data, and performs train_clasifier and predict_labels.
 - This function will report the F<sub>1</sub> score for both the training and testing data separately.
End of explanation
"""


# TODO: Import the three supervised learning models from sklearn
# from sklearn import model_A
# from sklearn import model_B
# from skearln import model_C
from sklearn.tree import DecisionTreeClassifier
from sklearn.svm import SVC
from sklearn.ensemble import AdaBoostClassifier

# TODO: Initialize the three models
clf_A = DecisionTreeClassifier(random_state = 42)
clf_B = SVC(random_state = 42)
clf_C = AdaBoostClassifier(random_state=42)

# TODO: Execute the 'train_predict' function for each classifier and each training set size
# train_predict(clf, X_train, y_train, X_test, y_test)

for clf in [clf_A, clf_B, clf_C]:
    print "\n{}: \n".format(clf.__class__.__name__)
    for n in [100, 200, 300]:
        train_predict(clf, X_train[:n], y_train[:n], X_test, y_test)
        


"""
Explanation: Implementation: Model Performance Metrics
With the predefined functions above, you will now import the three supervised learning models of your choice and run the train_predict function for each one. Remember that you will need to train and predict on each classifier for three different training set sizes: 100, 200, and 300. Hence, you should expect to have 9 different outputs below — 3 for each model using the varying training set sizes. In the following code cell, you will need to implement the following:
- Import the three supervised learning models you've discussed in the previous section.
- Initialize the three models and store them in clf_A, clf_B, and clf_C.
 - Use a random_state for each model you use, if provided.
 - Note: Use the default settings for each model — you will tune one specific model in a later section.
- Create the different training set sizes to be used to train each model.
 - Do not reshuffle and resplit the data! The new training points should be drawn from X_train and y_train.
- Fit each model with each training set size and make predictions on the test set (9 in total).
Note: Three tables are provided after the following code cell which can be used to store your results.
End of explanation
"""


# TODO: Import 'GridSearchCV' and 'make_scorer'
from sklearn.grid_search import GridSearchCV
from sklearn.metrics import make_scorer, f1_score
# TODO: Create the parameters list you wish to tune

parameters = {'C': range(1,10), 'random_state': [42], 'gamma': np.arange(0,1,0.1)}

# TODO: Initialize the classifier
clf = SVC()


# TODO: Make an f1 scoring function using 'make_scorer' 
f1_scorer = make_scorer(f1_score, pos_label='yes')

# TODO: Perform grid search on the classifier using the f1_scorer as the scoring method
grid_obj = GridSearchCV(estimator=clf, scoring= f1_scorer, param_grid=parameters )

# TODO: Fit the grid search object to the training data and find the optimal parameters
grid_obj = grid_obj.fit(X_train, y_train)

# Get the estimator
clf = grid_obj.best_estimator_

# Report the final F1 score for training and testing after parameter tuning
print "Tuned model has a training F1 score of {:.4f}.".format(predict_labels(clf, X_train, y_train))
print "Tuned model has a testing F1 score of {:.4f}.".format(predict_labels(clf, X_test, y_test))

"""
Explanation: Tabular Results
Edit the cell below to see how a table can be designed in Markdown. You can record your results from above in the tables provided.
 Classifer 1 - Decission Tree  
| Training Set Size | Training Time | Prediction Time (test) | F1 Score (train) | F1 Score (test) |
| :---------------: | :---------------------: | :--------------------: | :--------------: | :-------------: |
| 100               |           0.0010        |           0.0000       |         1.0      |     0.6452            |
| 200               |           0.0010        |           0.0000       |         1.0      |     0.7258    |
| 300               |           0.0020        |           0.0000       |         1.0      |    0.6838      |
 Classifer 2 -SVM   
| Training Set Size | Training Time | Prediction Time (test) | F1 Score (train) | F1 Score (test) |
| :---------------: | :---------------------: | :--------------------: | :--------------: | :-------------: |
| 100               |     0.0010              |       0.0000           |  0.8354          |   0.8025    |
| 200               |     0.0050              |       0.0010           |  0.8431          |   0.8105    |
| 300               |     0.0080              |       0.0020           |  0.8664          |   0.8052         |
 Classifer 3 - Boosting  
| Training Set Size | Training Time | Prediction Time (test) | F1 Score (train) | F1 Score (test) |
| :---------------: | :---------------------: | :--------------------: | :--------------: | :-------------: |
| 100               |         0.0960           |     0.0050             |    0.9778       | 0.6880     |
| 200               |        0.1260            |     0.0040             |    0.8905       |0.7445     |
| 300               |          0.1220          |       0.0080           |   0.8565        | 0.7328    |
Choosing the Best Model
In this final section, you will choose from the three supervised learning models the best model to use on the student data. You will then perform a grid search optimization for the model over the entire training set (X_train and y_train) by tuning at least one parameter to improve upon the untuned model's F<sub>1</sub> score. 
Question 3 - Chosing the Best Model
Based on the experiments you performed earlier, in one to two paragraphs, explain to the board of supervisors what single model you chose as the best model. Which model is generally the most appropriate based on the available data, limited resources, cost, and performance?
Answer: 
Based on the experiments done and the results table above SVM model is giving the most accurate prediction in a reasonable trainig and testing time. Since this data set was small the time diffrents may not seem an issue for comparison but if we get more and more data then for sure it can cause some problems. By coparing Decision Tree model and SVM which both has almost same running time but SVM turn out to be more accurate. And also it is obvious from the results that Decision Tree is highly dependant on data and in terms of machine learning is overfitted because it gives a 100% accurate result on the trained data it shows that it was not able to generelize the prediction. Therefore my choice is SVM model.   
Question 4 - Model in Layman's Terms
In one to two paragraphs, explain to the board of directors in layman's terms how the final model chosen is supposed to work. For example if you've chosen to use a decision tree or a support vector machine, how does the model go about making a prediction?
Answer: 
Support Vector Machine is a classification algorithm which simply means it classify data into diffrent categories (in our case we have two categories "passed" and "not passed"). But how does this algorithm work? Well, the main goal of this algorithm is to find a seperating vector which will group all the data. For simplicity lets assume that we only have 2 features and when we draw the scatter plot we see that the data are divided to 2 diffrent groups which we can seperate using a line, so in this case our goal is to find the maximum margin between these two group of data. As a result in the future when we have a new data point we simply predict the associated labele to it based on it's location regarding to line. Its good to mention that when we have more features the line changes to a plane which seperates data exactly the same way but in higher dimention.
Implementation: Model Tuning
Fine tune the chosen model. Use grid search (GridSearchCV) with at least one important parameter tuned with at least 3 different values. You will need to use the entire training set for this. In the code cell below, you will need to implement the following:
- Import sklearn.grid_search.gridSearchCV and sklearn.metrics.make_scorer.
- Create a dictionary of parameters you wish to tune for the chosen model.
 - Example: parameters = {'parameter' : [list of values]}.
- Initialize the classifier you've chosen and store it in clf.
- Create the F<sub>1</sub> scoring function using make_scorer and store it in f1_scorer.
 - Set the pos_label parameter to the correct value!
- Perform grid search on the classifier clf using f1_scorer as the scoring method, and store it in grid_obj.
- Fit the grid search object to the training data (X_train, y_train), and store it in grid_obj.
End of explanation
"""


