import numpy as np
import pandas as pd
from IPython.display import display

# plotting modules
import seaborn as sns
import matplotlib.pyplot as plt
import self_made_visual as vs

%matplotlib inline

from time import time

# set seed for reproducibility
np.random.seed(45)

try:
    data = pd.read_csv("winequality_red.csv")
    print("Red Wine Quality dataset has {} samples with {} features each.".format(*data.shape))
    display(data.head())
except:
    print("Dataset could not be loaded. Is the dataset missing?")

# Checking for missing values
# Calculating total number of missing values in dataset

missingValues = data.isnull().sum()

display("Total number of missing values in our dataset are:", missingValues)

# Displaying description of data-set

percent = [0.04, 0.25, 0.5, 0.75, 0.96]
display(data.describe(percentiles=percent))

# From sklear we are importing 'preprocessing'
# Further MinMaxScaler() will be used for scaling data between [0,1]

from sklearn import preprocessing

# Setting all the column names in a variable 'features'
features = data.keys()

min_max_scaler = preprocessing.MinMaxScaler()
data_array_scaled = min_max_scaler.fit_transform(data)

# Creating an empty data frame
data_scaled = pd.DataFrame()

for x in range(0,12):
    dataset = pd.DataFrame({features[x]:data_array_scaled[:,x]})
    data_scaled = pd.concat([data_scaled, dataset], axis=1, join_axes=[dataset.index])
    
display(data_scaled.head())

# Produce a scatter matrix for each pair of newly-transformed features
pd.plotting.scatter_matrix(data, alpha = 0.3, figsize = (50,30), diagonal = 'kde');

# Creating a empty figure then creating bar-graph using Seaborn
fig = plt.figure(figsize = (15,8))
sns.barplot(x = 'quality', y = 'fixed acidity', data = data)

fig = plt.figure(figsize = (15,8))
sns.barplot(x = 'quality', y = 'volatile acidity', data = data)

fig = plt.figure(figsize = (15,8))
sns.barplot(x = 'quality', y = 'citric acid', data = data)

fig = plt.figure(figsize = (15,8))
sns.barplot(x = 'quality', y = 'residual sugar', data = data)

fig = plt.figure(figsize = (15,8))
sns.barplot(x = 'quality', y = 'chlorides', data = data)

fig = plt.figure(figsize = (15,8))
sns.barplot(x = 'quality', y = 'free sulfur dioxide', data = data)

fig = plt.figure(figsize = (15,8))
sns.barplot(x = 'quality', y = 'total sulfur dioxide', data = data)

fig = plt.figure(figsize = (15,8))
sns.barplot(x = 'quality', y = 'density', data = data)

fig = plt.figure(figsize = (15,8))
sns.barplot(x = 'quality', y = 'pH', data = data)

fig = plt.figure(figsize = (15,8))
sns.barplot(x = 'quality', y = 'sulphates', data = data)

fig = plt.figure(figsize = (15,8))
sns.barplot(x = 'quality', y = 'alcohol', data = data)

data.drop(['density'], axis = 1, inplace = True)
display(data.head())

# Dividing Quality into two labels -- Bad & Good

bucket = (2, 6.1, 8)
bucket_label = ['bad', 'good']
data['quality'] = pd.cut(data['quality'], bins = bucket, labels = bucket_label)
display(data.head())

data['quality'] = data['quality'].map({'bad':0,'good':1})
display(data.head(10))

# calculating count
display("Total number of different Wine samples: ",data['quality'].value_counts())

# representing counts using bar graph
sns.countplot(data['quality'])

from sklearn.metrics import fbeta_score, accuracy_score 

def train_predict(learner, sample_size, X_train, y_train, X_test, y_test): 
    
    results = {}
    
    #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
    
    #Calculate the training time
    results['train_time'] = end - start
        
    # 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
    
    # Calculate the total prediction time
    results['pred_time'] = end - start
            
    # Compute accuracy on the first 300 training samples which is y_train[:300]
    results['acc_train'] = accuracy_score(y_train[:300], predictions_train[:300])
        
    # Compute accuracy on test set using accuracy_score()
    results['acc_test'] = accuracy_score(y_test, predictions_test)
    
    # 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)
        
    # 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

# Import train_test_split
from sklearn.cross_validation import train_test_split

X = data.drop('quality', axis = 1)
y = data['quality']

# Split the 'features' and 'income' data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, 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]))

# Import two supervised learning models from sklearn
from sklearn.ensemble import AdaBoostClassifier
from sklearn.svm import SVC

# Initialize the two models
clf_A = AdaBoostClassifier(random_state=40)
clf_B = SVC(random_state=35)

# Calculate the number of samples for 1%, 10%, and 100% of the training data
samples_100 = X_train.shape[0]
samples_10 = X_train.shape[0]//10
samples_1 = X_train.shape[0]//100

# Collect results on the learners
results = {}
for clf in [clf_A, clf_B]:
    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.visualize(results)

# Import 'GridSearchCV', 'make_scorer'
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import make_scorer

# Suppressing warnings
import warnings
warnings.filterwarnings('ignore')

# TODO: Initialize the classifier
clf = SVC(random_state=35)

# Create the parameters list you wish to tune, using a dictionary if needed.
parameters = {
    'kernel':['rbf'],
    'gamma' :[0.5,0.8,0.9,1,1.1,1.2,1.3, 10, 100, 200]
}

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

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

# 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)))

#Parameter value

print("gamma = {}".format(best_clf.get_params()['gamma']))