finding_donors.py

# coding: utf-8

# ## Supervised Learning
# ## Project: Finding Donors for *CharityML*

# 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:** Please specify WHICH VERSION OF PYTHON you are using when submitting this notebook. 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.

# ## Getting Started
# 
# In this project, you will employ several supervised algorithms of your choice to accurately model individuals' income using data collected from the 1994 U.S. Census. You will then choose the best candidate algorithm from preliminary results and further optimize this algorithm to best model the data. Your goal with this implementation is to construct a model that accurately predicts whether an individual makes more than $50,000. This sort of task can arise in a non-profit setting, where organizations survive on donations.  Understanding an individual's income can help a non-profit better understand how large of a donation to request, or whether or not they should reach out to begin with.  While it can be difficult to determine an individual's general income bracket directly from public sources, we can (as we will see) infer this value from other publically available features. 
# 
# The dataset for this project originates from the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/Census+Income). The datset was donated by Ron Kohavi and Barry Becker, after being published in the article _"Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid"_. You can find the article by Ron Kohavi [online](https://www.aaai.org/Papers/KDD/1996/KDD96-033.pdf). The data we investigate here consists of small changes to the original dataset, such as removing the `'fnlwgt'` feature and records with missing or ill-formatted entries.

# ----
# ## Exploring the Data
# Run the code cell below to load necessary Python libraries and load the census data. Note that the last column from this dataset, `'income'`, will be our target label (whether an individual makes more than, or at most, $50,000 annually). All other columns are features about each individual in the census database.

# In[1]:


# Import libraries necessary for this project
import numpy as np
import pandas as pd
from time import time
from IPython.display import display # Allows the use of display() for DataFrames

# Import supplementary visualization code visuals.py
import visuals as vs

# Pretty display for notebooks
get_ipython().run_line_magic('matplotlib', 'inline')

# Load the Census dataset
data = pd.read_csv("census.csv")

# Success - Display the first record
display(data.head(n=1))


# ### Implementation: Data Exploration
# A cursory investigation of the dataset will determine how many individuals fit into either group, and will tell us about the percentage of these individuals making more than \$50,000. In the code cell below, you will need to compute the following:
# - The total number of records, `'n_records'`
# - The number of individuals making more than \$50,000 annually, `'n_greater_50k'`.
# - The number of individuals making at most \$50,000 annually, `'n_at_most_50k'`.
# - The percentage of individuals making more than \$50,000 annually, `'greater_percent'`.
# 
# ** HINT: ** You may need to look at the table above to understand how the `'income'` entries are formatted. 

# In[2]:


# TODO: Total number of records
n_records = data.shape[0]

# TODO: Number of records where individual's income is more than $50,000
# n_greater_50k = data[data['income'] == '>50K'].shape[0]

# TODO: Number of records where individual's income is at most $50,000
# n_at_most_50k = data[data['income'] == '<=50K'].shape[0]

n_at_most_50k, n_greater_50k = data.income.value_counts()

# TODO: Percentage of individuals whose income is more than $50,000
greater_percent = (n_greater_50k / n_records ) *100

# Print the results
print("Total number of records: {}".format(n_records))
print("Individuals making more than $50,000: {}".format(n_greater_50k))
print("Individuals making at most $50,000: {}".format(n_at_most_50k))
print("Percentage of individuals making more than $50,000: {}%".format(greater_percent))


# ** Featureset Exploration **
# 
# * **age**: continuous. 
# * **workclass**: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked. 
# * **education**: Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool. 
# * **education-num**: continuous. 
# * **marital-status**: Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse. 
# * **occupation**: Tech-support, Craft-repair, Other-service, Sales, Exec-managerial, Prof-specialty, Handlers-cleaners, Machine-op-inspct, Adm-clerical, Farming-fishing, Transport-moving, Priv-house-serv, Protective-serv, Armed-Forces. 
# * **relationship**: Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried. 
# * **race**: Black, White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other. 
# * **sex**: Female, Male. 
# * **capital-gain**: continuous. 
# * **capital-loss**: continuous. 
# * **hours-per-week**: continuous. 
# * **native-country**: United-States, Cambodia, England, Puerto-Rico, Canada, Germany, Outlying-US(Guam-USVI-etc), India, Japan, Greece, South, China, Cuba, Iran, Honduras, Philippines, Italy, Poland, Jamaica, Vietnam, Mexico, Portugal, Ireland, France, Dominican-Republic, Laos, Ecuador, Taiwan, Haiti, Columbia, Hungary, Guatemala, Nicaragua, Scotland, Thailand, Yugoslavia, El-Salvador, Trinadad&Tobago, Peru, Hong, Holand-Netherlands.

# ----
# ## Preparing the Data
# Before data can be used as input for machine learning algorithms, it often must be cleaned, formatted, and restructured — this is typically known as **preprocessing**. Fortunately, for this dataset, there are no invalid or missing entries we must deal with, however, there are some qualities about certain features that must be adjusted. This preprocessing can help tremendously with the outcome and predictive power of nearly all learning algorithms.

# ### Transforming Skewed Continuous Features
# A dataset may sometimes contain at least one feature whose values tend to lie near a single number, but will also have a non-trivial number of vastly larger or smaller values than that single number.  Algorithms can be sensitive to such distributions of values and can underperform if the range is not properly normalized. With the census dataset two features fit this description: '`capital-gain'` and `'capital-loss'`. 
# 
# Run the code cell below to plot a histogram of these two features. Note the range of the values present and how they are distributed.

# In[3]:


# Split the data into features and target label
income_raw = data['income']
features_raw = data.drop('income', axis = 1)

# Visualize skewed continuous features of original data
vs.distribution(data)


# For highly-skewed feature distributions such as `'capital-gain'` and `'capital-loss'`, it is common practice to apply a <a href="https://en.wikipedia.org/wiki/Data_transformation_(statistics)">logarithmic transformation</a> on the data so that the very large and very small values do not negatively affect the performance of a learning algorithm. Using a logarithmic transformation significantly reduces the range of values caused by outliers. Care must be taken when applying this transformation however: The logarithm of `0` is undefined, so we must translate the values by a small amount above `0` to apply the the logarithm successfully.
# 
# Run the code cell below to perform a transformation on the data and visualize the results. Again, note the range of values and how they are distributed. 

# In[4]:


# Log-transform the skewed features
skewed = ['capital-gain', 'capital-loss']
features_log_transformed = pd.DataFrame(data = features_raw)
features_log_transformed[skewed] = features_raw[skewed].apply(lambda x: np.log(x + 1))

# Visualize the new log distributions
vs.distribution(features_log_transformed, transformed = True)


# ### Normalizing Numerical Features
# In addition to performing transformations on features that are highly skewed, it is often good practice to perform some type of scaling on numerical features. Applying a scaling to the data does not change the shape of each feature's distribution (such as `'capital-gain'` or `'capital-loss'` above); however, normalization ensures that each feature is treated equally when applying supervised learners. Note that once scaling is applied, observing the data in its raw form will no longer have the same original meaning, as exampled below.
# 
# Run the code cell below to normalize each numerical feature. We will use [`sklearn.preprocessing.MinMaxScaler`](http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html) for this.

# In[5]:


# Import sklearn.preprocessing.StandardScaler
from sklearn.preprocessing import MinMaxScaler

# Initialize a scaler, then apply it to the features
scaler = MinMaxScaler() # default=(0, 1)
numerical = ['age', 'education-num', 'capital-gain', 'capital-loss', 'hours-per-week']

features_log_minmax_transform = pd.DataFrame(data = features_log_transformed)
features_log_minmax_transform[numerical] = scaler.fit_transform(features_log_transformed[numerical])

# Show an example of a record with scaling applied
display(features_log_minmax_transform.head(n = 5))


# ### Implementation: Data Preprocessing
# 
# From the table in **Exploring the Data** above, we can see there are several features for each record that are non-numeric. Typically, learning algorithms expect input to be numeric, which requires that non-numeric features (called *categorical variables*) be converted. One popular way to convert categorical variables is by using the **one-hot encoding** scheme. One-hot encoding creates a _"dummy"_ variable for each possible category of each non-numeric feature. For example, assume `someFeature` has three possible entries: `A`, `B`, or `C`. We then encode this feature into `someFeature_A`, `someFeature_B` and `someFeature_C`.
# 
# |   | someFeature |                    | someFeature_A | someFeature_B | someFeature_C |
# | :-: | :-: |                            | :-: | :-: | :-: |
# | 0 |  B  |  | 0 | 1 | 0 |
# | 1 |  C  | ----> one-hot encode ----> | 0 | 0 | 1 |
# | 2 |  A  |  | 1 | 0 | 0 |
# 
# Additionally, as with the non-numeric features, we need to convert the non-numeric target label, `'income'` to numerical values for the learning algorithm to work. Since there are only two possible categories for this label ("<=50K" and ">50K"), we can avoid using one-hot encoding and simply encode these two categories as `0` and `1`, respectively. In code cell below, you will need to implement the following:
#  - Use [`pandas.get_dummies()`](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.get_dummies.html?highlight=get_dummies#pandas.get_dummies) to perform one-hot encoding on the `'features_log_minmax_transform'` data.
#  - Convert the target label `'income_raw'` to numerical entries.
#    - Set records with "<=50K" to `0` and records with ">50K" to `1`.

# In[6]:


# TODO: One-hot encode the 'features_log_minmax_transform' data using pandas.get_dummies()
features_final = pd.get_dummies(features_log_minmax_transform)

# TODO: Encode the 'income_raw' data to numerical values
income = income_raw.apply(lambda x: 0 if x == '<=50K' else 1)

# Print the number of features after one-hot encoding
encoded = list(features_final.columns)
print("{} total features after one-hot encoding.".format(len(encoded)))

# Uncomment the following line to see the encoded feature names
print(encoded)


# ### Shuffle and Split Data
# Now all _categorical variables_ have been converted into numerical features, and all numerical features have been normalized. As always, we will now split the data (both features and their labels) into training and test sets. 80% of the data will be used for training and 20% for testing.
# 
# Run the code cell below to perform this split.

# In[7]:


# Import train_test_split
# from sklearn.cross_validation import train_test_split
from sklearn.model_selection import train_test_split

# Split the 'features' and 'income' data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(features_final, 
                                                    income, 
                                                    test_size = 0.2, 
                                                    random_state = 0)

# 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]))


# ----
# ## Evaluating Model Performance
# In this section, we will investigate four different algorithms, and determine which is best at modeling the data. Three of these algorithms will be supervised learners of your choice, and the fourth algorithm is known as a *naive predictor*.

# ### Metrics and the Naive Predictor
# *CharityML*, equipped with their research, knows individuals that make more than \$50,000 are most likely to donate to their charity. Because of this, *CharityML* is particularly interested in predicting who makes more than \$50,000 accurately. It would seem that using **accuracy** as a metric for evaluating a particular model's performace would be appropriate. Additionally, identifying someone that *does not* make more than \$50,000 as someone who does would be detrimental to *CharityML*, since they are looking to find individuals willing to donate. Therefore, a model's ability to precisely predict those that make more than \$50,000 is *more important* than the model's ability to **recall** those individuals. We can use **F-beta score** as a metric that considers both precision and recall:
# 
# $$ F_{\beta} = (1 + \beta^2) \cdot \frac{precision \cdot recall}{\left( \beta^2 \cdot precision \right) + recall} $$
# 
# In particular, when $\beta = 0.5$, more emphasis is placed on precision. This is called the **F$_{0.5}$ score** (or F-score for simplicity).
# 
# Looking at the distribution of classes (those who make at most \$50,000, and those who make more), it's clear most individuals do not make more than \$50,000. This can greatly affect **accuracy**, since we could simply say *"this person does not make more than \$50,000"* and generally be right, without ever looking at the data! Making such a statement would be called **naive**, since we have not considered any information to substantiate the claim. It is always important to consider the *naive prediction* for your data, to help establish a benchmark for whether a model is performing well. That been said, using that prediction would be pointless: If we predicted all people made less than \$50,000, *CharityML* would identify no one as donors. 
# 
# 
# #### Note: Recap of accuracy, precision, recall
# 
# ** Accuracy ** measures how often the classifier makes the correct prediction. It’s the ratio of the number of correct predictions to the total number of predictions (the number of test data points).
# 
# ** Precision ** tells us what proportion of messages we classified as spam, actually were spam.
# It is a ratio of true positives(words classified as spam, and which are actually spam) to all positives(all words classified as spam, irrespective of whether that was the correct classificatio), in other words it is the ratio of
# 
# `[True Positives/(True Positives + False Positives)]`
# 
# ** Recall(sensitivity)** tells us what proportion of messages that actually were spam were classified by us as spam.
# It is a ratio of true positives(words classified as spam, and which are actually spam) to all the words that were actually spam, in other words it is the ratio of
# 
# `[True Positives/(True Positives + False Negatives)]`
# 
# For classification problems that are skewed in their classification distributions like in our case, for example if we had a 100 text messages and only 2 were spam and the rest 98 weren't, accuracy by itself is not a very good metric. We could classify 90 messages as not spam(including the 2 that were spam but we classify them as not spam, hence they would be false negatives) and 10 as spam(all 10 false positives) and still get a reasonably good accuracy score. For such cases, precision and recall come in very handy. These two metrics can be combined to get the F1 score, which is weighted average(harmonic mean) of the precision and recall scores. This score can range from 0 to 1, with 1 being the best possible F1 score(we take the harmonic mean as we are dealing with ratios).

# ### Question 1 - Naive Predictor Performace
# * If we chose a model that always predicted an individual made more than $50,000, what would  that model's accuracy and F-score be on this dataset? You must use the code cell below and assign your results to `'accuracy'` and `'fscore'` to be used later.
# 
# ** Please note ** that the the purpose of generating a naive predictor is simply to show what a base model without any intelligence would look like. In the real world, ideally your base model would be either the results of a previous model or could be based on a research paper upon which you are looking to improve. When there is no benchmark model set, getting a result better than random choice is a place you could start from.
# 
# ** HINT: ** 
# 
# * When we have a model that always predicts '1' (i.e. the individual makes more than 50k) then our model will have no True Negatives(TN) or False Negatives(FN) as we are not making any negative('0' value) predictions. Therefore our Accuracy in this case becomes the same as our Precision(True Positives/(True Positives + False Positives)) as every prediction that we have made with value '1' that should have '0' becomes a False Positive; therefore our denominator in this case is the total number of records we have in total. 
# * Our Recall score(True Positives/(True Positives + False Negatives)) in this setting becomes 1 as we have no False Negatives.

# In[8]:


'''
TP = np.sum(income) # Counting the ones as this is the naive case. Note that 'income' is the 'income_raw' data 
encoded to numerical values done in the data preprocessing step.
FP = income.count() - TP # Specific to the naive case

TN = 0 # No predicted negatives in the naive case
FN = 0 # No predicted negatives in the naive case
'''
TP = np.sum(income)
FP = income.count() - TP
TN = 0
FN = 0

# TODO: Calculate accuracy, precision and recall
accuracy = TP / income.count()
recall = TP / (TP + FN)
precision = TP / (TP + FP)

# TODO: Calculate F-score using the formula above for beta = 0.5 and correct values for precision and recall.
beta = 0.5
fscore = (1 + beta**2) * precision * recall / ((beta**2)*precision + recall)

# Print the results 
print("Naive Predictor: [Accuracy score: {:.4f}, F-score: {:.4f}]".format(accuracy, fscore))


# ###  Supervised Learning Models
# **The following are some of the supervised learning models that are currently available in** [`scikit-learn`](http://scikit-learn.org/stable/supervised_learning.html) **that you may choose from:**
# - Gaussian Naive Bayes (GaussianNB)
# - Decision Trees
# - Ensemble Methods (Bagging, AdaBoost, Random Forest, Gradient Boosting)
# - K-Nearest Neighbors (KNeighbors)
# - Stochastic Gradient Descent Classifier (SGDC)
# - Support Vector Machines (SVM)
# - Logistic Regression

# ### Question 2 - Model Application
# List three of the supervised learning models above that are appropriate for this problem that you will test on the census data. For each model chosen
# 
# - Describe one real-world application in industry where the model can be applied. 
# - What are the strengths of the model; when does it perform well?
# - What are the weaknesses of the model; when does it perform poorly?
# - What makes this model a good candidate for the problem, given what you know about the data?
# 
# ** HINT: **
# 
# Structure your answer in the same format as above^, with 4 parts for each of the three models you pick. Please include references with your answer.

# **Answer: **
# 
# The three supervised learning models that I would pick on the census data will be:
# 
# **Stochastic Gradient Descent (SGD) **
# 
# - Real-World Application: SGD can be applied in industry for evaluation of performance contribution of employee in any organization.
# 
# - Strength: SGD frequently updates changes in parameters of the model while training which causes detailed rate of improvement. Due to the randomness, it can avoid local minima better than regular Gradient Descent.
# 
# - Weakness: Convergence rate is slow and sensitive to feature scaling. Frequent updates are computationally expensive.
# 
# - Applicability: Stochastic gradient descent is a simple yet very efficient approach to fit linear models. It is particularly useful when the number of samples (and the number of features, here 103 after one-hot encoding) is very large. For the given dataset size, this model would be quite fast and converge quickly. Hence, this model could be a good candidate for this problem.
# 
# - References:
# http://iosrjournals.org/iosr-jbm/papers/Vol16-issue6/Version-3/I016637780.pdf
# https://towardsdatascience.com/gradient-descent-in-a-nutshell-eaf8c18212f0
# 
# 
# 
# **Gradient Boosting **
# 
# - Real-World Application: Gradient boosting can be used in developing machine-learned ranking search engines.
# 
# - Strength: This algorithm builds or improves the model in such a way that it complements the previously built sequence of model in an effort to correct or improve the final output of the model. Natural handling of data of mixed type (= heterogeneous features).
# 
# - Weakness: The weaknesses of this model are that it is harder to run parallely and hence are time consuming. They are also little prone to overfitting and there are more parameters to tune.
# 
# - Applicability: This algorithm makes use of other weaker algorithms typically Decision Tree, improves them to become better by learning progress and finally combine their results to get best model. Our dataset has large number of features with non-linear relations between them. Decision Tree implicitly perform variable screening or feature selection. Also, non-linear relationships between parameters do not affect tree performance. Since decision tree is suitable for our dataset, using number of such trees which is improving one after another makes gradient boosting one of best suitable candidate for this dataset.
# 
# - References:
# http://proceedings.mlr.press/v14/chapelle11a/chapelle11a.pdf?WT.mc_id=Blog_MachLearn_General_DI
# https://machinelearningmastery.com/gentle-introduction-gradient-boosting-algorithm-machine-learning/
# 
# 
# **Logistic Regression **
# 
# - Real-World Application: This has many industrial applications. One application in medical research is the Trauma and Injury Severity Score (TRISS), which is widely used to predict mortality in injured patients. Widely used in building neural networks in deep learning.
# 
# - Strength: This is simple, fast and efficient for small dataset with limited features.
# 
# - Weakness: Adding more features to the model can result in overfitting, which reduces the generalizability of the model beyond the data on which the model is fit. Outliers in data adversely affect the model.
# 
# - Applicability: This is the one of the simplest model for binary classification (for our dataset, person earning less or equal to 50k or more than 50k). Also it is a good baseline to measure the performance of other more complex Algorithms. So choosing this one is must.
# 
# - References: 
# https://www.ncbi.nlm.nih.gov/pubmed/3106646
# https://en.wikipedia.org/wiki/Logistic_regression
# 

# ### Implementation - Creating a Training and Predicting Pipeline
# To properly evaluate the performance of each model you've chosen, it's important that you create a training and predicting pipeline that allows you to quickly and effectively train models using various sizes of training data and perform predictions on the testing data. Your implementation here will be used in the following section.
# In the code block below, you will need to implement the following:
#  - Import `fbeta_score` and `accuracy_score` from [`sklearn.metrics`](http://scikit-learn.org/stable/modules/classes.html#sklearn-metrics-metrics).
#  - Fit the learner to the sampled training data and record the training time.
#  - Perform predictions on the test data `X_test`, and also on the first 300 training points `X_train[:300]`.
#    - Record the total prediction time.
#  - Calculate the accuracy score for both the training subset and testing set.
#  - Calculate the F-score for both the training subset and testing set.
#    - Make sure that you set the `beta` parameter!

# In[9]:


# TODO: Import two metrics from sklearn - fbeta_score and accuracy_score
from sklearn.metrics import fbeta_score
from sklearn.metrics import accuracy_score

def train_predict(learner, sample_size, X_train, y_train, X_test, y_test): 
    '''
    inputs:
       - learner: the learning algorithm to be trained and predicted on
       - sample_size: the size of samples (number) to be drawn from training set
       - X_train: features training set
       - y_train: income training set
       - X_test: features testing set
       - y_test: income testing set
    '''
    
    results = {}
    
    # TODO: Fit the learner to the training data using slicing with 'sample_size' using .fit(training_features[:], training_labels[:])
    start = time() # Get start time
    learner = learner.fit(X_train[:sample_size], y_train[:sample_size])
    end = time() # Get end time
    
    # TODO: Calculate the training time
    results['train_time'] = end - start
        
    # TODO: Get the predictions on the test set(X_test),
    #       then get predictions on the first 300 training samples(X_train) using .predict()
    start = time() # Get start time
    predictions_test = learner.predict(X_test)
    predictions_train = learner.predict(X_train[:300])
    end = time() # Get end time
    
    # TODO: Calculate the total prediction time
    results['pred_time'] = end - start
            
    # TODO: Compute accuracy on the first 300 training samples which is y_train[:300]
    results['acc_train'] = accuracy_score(y_train[:300], predictions_train)
        
    # TODO: Compute accuracy on test set using accuracy_score()
    results['acc_test'] = accuracy_score(y_test, predictions_test)
    
    # TODO: Compute F-score on the the first 300 training samples using fbeta_score()
    results['f_train'] = fbeta_score(y_train[:300], predictions_train, beta=0.5)
        
    # TODO: Compute F-score on the test set which is y_test
    results['f_test'] = fbeta_score(y_test, predictions_test, beta=0.5)
       
    # Success
    print("{} trained on {} samples.".format(learner.__class__.__name__, sample_size))
        
    # Return the results
    return results


# ### Implementation: Initial Model Evaluation
# In the 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.
# - Calculate the number of records equal to 1%, 10%, and 100% of the training data.
#   - Store those values in `'samples_1'`, `'samples_10'`, and `'samples_100'` respectively.
# 
# **Note:** Depending on which algorithms you chose, the following implementation may take some time to run!

# In[10]:


# TODO: Import the three supervised learning models from sklearn
# from sklearn.ensemble import RandomForestClassifier
# from sklearn.ensemble import AdaBoostClassifier
# from sklearn.svm import SVC
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import SGDClassifier
from sklearn.ensemble import GradientBoostingClassifier


# TODO: Initialize the three models
clf_A = LogisticRegression(random_state = 0)
clf_B = GradientBoostingClassifier(random_state = 0)
clf_C = SGDClassifier(random_state = 0)

# TODO: Calculate the number of samples for 1%, 10%, and 100% of the training data
# HINT: samples_100 is the entire training set i.e. len(y_train)
# HINT: samples_10 is 10% of samples_100 (ensure to set the count of the values to be `int` and not `float`)
# HINT: samples_1 is 1% of samples_100 (ensure to set the count of the values to be `int` and not `float`)
samples_100 = X_train.shape[0]
samples_10 = samples_100 // 10
samples_1 = samples_100 // 100

# Collect results on the learners
results = {}
for clf in [clf_A, clf_B, clf_C]:
    clf_name = clf.__class__.__name__
    results[clf_name] = {}
    for i, samples in enumerate([samples_1, samples_10, samples_100]):
        results[clf_name][i] =         train_predict(clf, samples, X_train, y_train, X_test, y_test)

# Run metrics visualization for the three supervised learning models chosen
vs.evaluate(results, accuracy, fscore)


# ----
# ## Improving Results
# 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-score. 

# ### Question 3 - Choosing the Best Model
# 
# * Based on the evaluation you performed earlier, in one to two paragraphs, explain to *CharityML* which of the three models you believe to be most appropriate for the task of identifying individuals that make more than \$50,000. 
# 
# ** HINT: ** 
# Look at the graph at the bottom left from the cell above(the visualization created by `vs.evaluate(results, accuracy, fscore)`) and check the F score for the testing set when 100% of the training set is used. Which model has the highest score? Your answer should include discussion of the:
# * metrics - F score on the testing when 100% of the training data is used, 
# * prediction/training time
# * the algorithm's suitability for the data.

# **Answer: **
# 
# As can been seen in the above plots, the Gradient Boosting model has highest Accuracy as well as F-score. Thus we believe this model to be the most appropriate among the three selected models.
# 
# Though the model comparatively takes lot of training/prediction time than the other two, it is still very less. Considering the limited size of this dataset and intention of output being independent of time, this model can be considered suitable. Since we are more interested in maximizing the number of accurately classified people that make more than 50K, this model, which has the highest accuracy, seems to be the most appropriate.
# 
# Also, having significantly higher F-score states that, model is doing good in terms of both recall and precision.

# ### Question 4 - Describing the Model in Layman's Terms
# 
# * In one to two paragraphs, explain to *CharityML*, in layman's terms, how the final model chosen is supposed to work. Be sure that you are describing the major qualities of the model, such as how the model is trained and how the model makes a prediction. Avoid using advanced mathematical jargon, such as describing equations.
# 
# ** HINT: **
# 
# When explaining your model, if using external resources please include all citations.

# **Answer: ** 
# 
# The Gradient Boosting algorithm in layman's terms is basically a child who always learns from mistakes and experiences. A child doing something new, first takes little steps and on getting favourable output goes on doing it. When makes mistakes, learns from it, improves the process and adds them all to experiences. There are family and teachers who supports and keep check on child so that the child stays on right path. On every step, the child is advised, suggested or even punished to become a good human being and adaptive to the society.
# Now, here the child is Decision Tree model who is weak and has very less or no experience. The family or teachers are here a loss function which keeps check on behaviour or progress of the weak model. On every step, a little piece of advice or suggestion as different parameters build the model more optimised and suitable to the dataset as society.
# 
# Being specific to the topic, Gradient boosting is a machine learning technique for regression and classification problems, which produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees. So, we first understand what decision tree is with the help of our data. Let's think of three persons, first one is a student, second and third are working professionals. We categorise them into two labels earning '<=50k' or '>50k' with features as work class (working or student) and their education level (having bachelor’s degree or not). First person being student, having no source of income, we put him under label earning less than '<=50k'. 2nd person who is in working class but don't have bachelor’s degree has no good job and earn less also labelled as '<=50k'. Now 3rd person, first falls into working class category and then fall into having bachelor’s degree earns good is labelled as '>50k'. So this forms a tree like structure shown below called decision tree. Multiple number of such trees are formed "sequentially" with one learning from mistakes made by previous one. This is called boosting. It converts sequence of weak learners into very complex learners. As can be seen in below flowchart, the tree improves itself and at last results the final tree.
# 
# <img src="finding_donors.png" alt="" height="1000" width="800">
# 
# Any supervised learning algorithm has a loss function and we want it as minimum. By using gradient descent and updating our predictions based on a learning rate, we can find the values where loss function is minimum. So, we are basically updating the predictions such that the sum of our residuals is close to 0 (or minimum) and predicted values are sufficiently close to actual values. So, the intuition behind gradient boosting algorithm is to repetitively leverage the patterns in residuals and strengthen a model with weak predictions and make it better. Once we reach a stage that residuals do not have any pattern that could be modelled, we can stop modelling residuals (otherwise it might lead to overfitting). Algorithmically, we are minimizing our loss function, such that test loss reach its minima. Summing up all, we first model data with simple models and analyse data for errors. These errors signify data points that are difficult to fit by a simple model. Then for later models, we particularly focus on those hard to fit data to get them right. In the end, we combine all the predictors by giving some weights to each predictor.
# 
# References:
# https://en.wikipedia.org/wiki/Gradient_boosting
# https://machinelearningmastery.com/gentle-introduction-gradient-boosting-algorithm-machine-learning/
# https://medium.com/mlreview/gradient-boosting-from-scratch-1e317ae4587d
# http://explained.ai/gradient-boosting/index.html

# ### 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`](http://scikit-learn.org/0.17/modules/generated/sklearn.grid_search.GridSearchCV.html) and [`sklearn.metrics.make_scorer`](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.make_scorer.html).
# - Initialize the classifier you've chosen and store it in `clf`.
#  - Set a `random_state` if one is available to the same state you set before.
# - Create a dictionary of parameters you wish to tune for the chosen model.
#  - Example: `parameters = {'parameter' : [list of values]}`.
#  - **Note:** Avoid tuning the `max_features` parameter of your learner if that parameter is available!
# - Use `make_scorer` to create an `fbeta_score` scoring object (with $\beta = 0.5$).
# - Perform grid search on the classifier `clf` using the `'scorer'`, and store it in `grid_obj`.
# - Fit the grid search object to the training data (`X_train`, `y_train`), and store it in `grid_fit`.
# 
# **Note:** Depending on the algorithm chosen and the parameter list, the following implementation may take some time to run!

# In[11]:


# TODO: Import 'GridSearchCV', 'make_scorer', and any other necessary libraries
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import make_scorer

# TODO: Initialize the classifier
#clf = AdaBoostClassifier(random_state = 0)
clf = GradientBoostingClassifier(random_state = 0)

# TODO: Create the parameters list you wish to tune, using a dictionary if needed.
# HINT: parameters = {'parameter_1': [value1, value2], 'parameter_2': [value1, value2]}
# parameters = {'n_estimators': [500,700], 'learning_rate': [1.25,1.5,1.75]}
parameters = {'learning_rate': [0.05, 0.07, 0.1],
              #'n_estimators': [100, 200, 300],
              'max_depth' : [3, 5, 7]
             }

# TODO: Make an fbeta_score scoring object using make_scorer()
scorer = make_scorer(fbeta_score, beta = 0.5)

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

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

# Get the estimator
best_clf = grid_fit.best_estimator_

# Make predictions using the unoptimized and model
predictions = (clf.fit(X_train, y_train)).predict(X_test)
best_predictions = best_clf.predict(X_test)

# Report the before-and-afterscores
print("Unoptimized model\n------")
print("Accuracy score on testing data: {:.4f}".format(accuracy_score(y_test, predictions)))
print("F-score on testing data: {:.4f}".format(fbeta_score(y_test, predictions, beta = 0.5)))
print("\nOptimized Model\n------")
print("Final accuracy score on the testing data: {:.4f}".format(accuracy_score(y_test, best_predictions)))
print("Final F-score on the testing data: {:.4f}".format(fbeta_score(y_test, best_predictions, beta = 0.5)))
print("\nUnoptimized Classifier\n------")
print(clf)
print("\nOptimized Classifier\n------")
print(best_clf)


# ### Question 5 - Final Model Evaluation
# 
# * What is your optimized model's accuracy and F-score on the testing data? 
# * Are these scores better or worse than the unoptimized model? 
# * How do the results from your optimized model compare to the naive predictor benchmarks you found earlier in **Question 1**?_  
# 
# **Note:** Fill in the table below with your results, and then provide discussion in the **Answer** box.

# #### Results:
# 
# |     Metric     | Unoptimized Model | Optimized Model |
# | :------------: | :---------------: | :-------------: | 
# | Accuracy Score |      0.8630       |     0.8700      |
# | F-score        |      0.7395       |     0.7496       |
# 

# **Answer: **
# 
# The optimized model's accuracy is 0.8700 and F-score is 0.7496 on the testing data.
# 
# The optimized model's scores are slightly better than unoptimized model. The optimized model's accuracy is increased by 0.81% and F-score is increased by 1.36% as compare to the unoptimized model.
# 
# The optimized model's scores are drastically improved than that of naive predictor benchmarks. The optimized model's accuracy is increased by 251% and F-score is increased by 156% as compare to the naive predictor benchmarks.

# ----
# ## Feature Importance
# 
# An important task when performing supervised learning on a dataset like the census data we study here is determining which features provide the most predictive power. By focusing on the relationship between only a few crucial features and the target label we simplify our understanding of the phenomenon, which is most always a useful thing to do. In the case of this project, that means we wish to identify a small number of features that most strongly predict whether an individual makes at most or more than \$50,000.
# 
# Choose a scikit-learn classifier (e.g., adaboost, random forests) that has a `feature_importance_` attribute, which is a function that ranks the importance of features according to the chosen classifier.  In the next python cell fit this classifier to training set and use this attribute to determine the top 5 most important features for the census dataset.

# ### Question 6 - Feature Relevance Observation
# When **Exploring the Data**, it was shown there are thirteen available features for each individual on record in the census data. Of these thirteen records, which five features do you believe to be most important for prediction, and in what order would you rank them and why?

# **Answer:**
# 
# The five most important features I believe for making prediction in descending order of their priorities are:
# 
# 1. Education-num: The higher the education, better the jobs, package and mentality. People with only higher education will understand the cause of charity i.e. to provide financial support for people eager to learn machine learning.
# 
# 2. Workclass : The knowledge of workclass is  important  as people with working class generally are supposed to donate. Students cannot donate.
# 
# 3. Captital loss:  Loss would give insight of person’s financial instability or debt. If there is loss of money, people generally refrain from donating in any kind of charity. Good mood is an important factor by the way.
# 
# 4. Capital gain: Profit shows how much an individual is financially stable. People with profit are in good mood and would like to donate their little gain into charity.
# 
# 5. Age: People with higher age have good salary and good savings. Moreover with age, comes great responsibility. 

# ### Implementation - Extracting Feature Importance
# Choose a `scikit-learn` supervised learning algorithm that has a `feature_importance_` attribute availble for it. This attribute is a function that ranks the importance of each feature when making predictions based on the chosen algorithm.
# 
# In the code cell below, you will need to implement the following:
#  - Import a supervised learning model from sklearn if it is different from the three used earlier.
#  - Train the supervised model on the entire training set.
#  - Extract the feature importances using `'.feature_importances_'`.

# In[12]:


# TODO: Import a supervised learning model that has 'feature_importances_'
from sklearn.ensemble import GradientBoostingClassifier

# TODO: Train the supervised model on the training set using .fit(X_train, y_train)
start = time()
model = GradientBoostingClassifier(random_state = 0)
model.fit(X_train, y_train)
end = time()
all_features_time = end - start

# TODO: Extract the feature importances using .feature_importances_ 
importances = model.feature_importances_

# Plot
vs.feature_plot(importances, X_train, y_train)


# ### Question 7 - Extracting Feature Importance
# 
# Observe the visualization created above which displays the five most relevant features for predicting if an individual makes at most or above \$50,000.  
# * How do these five features compare to the five features you discussed in **Question 6**?
# * If you were close to the same answer, how does this visualization confirm your thoughts? 
# * If you were not close, why do you think these features are more relevant?

# **Answer:**
# 
# Out of five features that visualization results, I guesses four as the most important features. Also my priorities for features are very much same as that of visualization result.
# 
# I guessed education-num as the most importatnt feature, which actually is. But the age which I predicted as of lowest importance out of five, resulted fourth most importatnt feature. 
# 
# I thought of workclass as an important feature, but from visualizaton, marital_status of individual plays an important role in deciding who is more liable to donate.
# 
# At last, I correctly predicted capital_loss and capital_gain as least important features among selected five.

# ### Feature Selection
# How does a model perform if we only use a subset of all the available features in the data? With less features required to train, the expectation is that training and prediction time is much lower — at the cost of performance metrics. From the visualization above, we see that the top five most important features contribute more than half of the importance of **all** features present in the data. This hints that we can attempt to *reduce the feature space* and simplify the information required for the model to learn. The code cell below will use the same optimized model you found earlier, and train it on the same training set *with only the top five important features*. 

# In[13]:


# Import functionality for cloning a model
from sklearn.base import clone

# Reduce the feature space
X_train_reduced = X_train[X_train.columns.values[(np.argsort(importances)[::-1])[:5]]]
X_test_reduced = X_test[X_test.columns.values[(np.argsort(importances)[::-1])[:5]]]

# Train on the "best" model found from grid search earlier
start = time()
clf = (clone(best_clf)).fit(X_train_reduced, y_train)
end = time()
reduced_features_time = end - start

# Make new predictions
reduced_predictions = clf.predict(X_test_reduced)

# Report scores from the final model using both versions of data
print("Final Model trained on full data\n------")
print("Accuracy on testing data: {:.4f}".format(accuracy_score(y_test, best_predictions)))
print("F-score on testing data: {:.4f}".format(fbeta_score(y_test, best_predictions, beta = 0.5)))
print("\nFinal Model trained on reduced data\n------")
print("Accuracy on testing data: {:.4f}".format(accuracy_score(y_test, reduced_predictions)))
print("F-score on testing data: {:.4f}".format(fbeta_score(y_test, reduced_predictions, beta = 0.5)))

#time difference between all_features model and reduced_features model
print("\nTime difference between all_features model and reduced_features set\n------")
print("Time executation for training all_features_model: {:.4f}".format(all_features_time))
print("Time executation for training reduced_features_model: {:.4f}".format(reduced_features_time))
print("Model training Time Difference: {:.4f}".format(all_features_time - reduced_features_time))


# ### Question 8 - Effects of Feature Selection
# 
# * How does the final model's F-score and accuracy score on the reduced data using only five features compare to those same scores when all features are used?
# * If training time was a factor, would you consider using the reduced data as your training set?

# **Answer:**
# 
# As compared to model's scores when all features are used, the accuracy score and F-score on the reduced data are slightly less. But the time of model training is significantly reduced with reduced features data set.
# 
# The accuracy is reduced by 1.32% and F-score is reduced by 3.25%.
# 
# If training time was an important factor, and slight reduction in score wasn't a big concern, I would have definately considered using the reduced data as my traing set.

# > **Note**: Once you have completed all of the code implementations and successfully answered each question above, you may finalize your work by exporting the iPython Notebook as an HTML document. You can do this by using the menu above and navigating to  
# **File -> Download as -> HTML (.html)**. Include the finished document along with this notebook as your submission.

# ##Before You Submit
# You will also need run the following in order to convert the Jupyter notebook into HTML, so that your submission will include both files.

# In[14]:


get_ipython().getoutput('jupyter nbconvert *.ipynb')