import warnings

import numpy as np
import pandas as pd

from time import time

import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap

from sklearn import datasets
from sklearn.decomposition import PCA
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LinearRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score
from sklearn.linear_model import Ridge
from sklearn.preprocessing import PolynomialFeatures
from sklearn.pipeline import make_pipeline

import Utils
from Utils import cmap_light
from Utils import cmap_bold 

"""
Explanation: Notes for me:
- Remove always show toolbar before starting
- Set zoom to 150%
- check there is internet
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Machine Learning with Python and Scikit Learn
Follow presentation on: http://bit.ly/ML-SpringCampus
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What is Machine Learning?
The process of teaching computers to learn from data.
Learning tasks:


Clustering


Regression


Outlier Detection


Classification


Time series prediction

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Supervised vs Unsupervised Learning
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Let's look at some code:
We will use:
 - Pandas: Python Data Analysis Library
 - Scikit-Learn
 - Numpy
 - Matplotlib
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Importing stuff...
End of explanation
"""


boston_dataset = datasets.load_boston()
print(boston_dataset.DESCR)

"""
Explanation: <br></br>
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Boston House Prices
End of explanation
"""


X = boston_dataset.data
Y = boston_dataset.target

names = list(boston_dataset.feature_names) + ['Price']

labels = np.reshape(Y,
                     (Y.shape[0], 1))
df = pd.DataFrame(data=np.concatenate((X, labels), axis=1),
                 columns=names)
df.head(10)

df_tmp = df[['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX',
             'RM', 'Price']]
df_tmp.head(10)

"""
Explanation: <br></br>
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The data
Dataset: 
a set of examples each characterized by features and  usually there is a label variable that you want to predict. 
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Let's see a real example:
End of explanation
"""


df_tmp.describe()

"""
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End of explanation
"""


from Utils import plot_boston_dataset
plot_boston_dataset(boston_dataset.data, 
                    boston_dataset.target)

"""
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End of explanation
"""


model = LinearRegression()

model.fit(X, Y)

r2 = model.score(X, Y)

print("R^2 value: {:0.3f}".format(r2))


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First model:  Linear Regression
<img src="https://imgs.xkcd.com/comics/linear_regression.png" title="XKCD: Linear Regression" width="512" height="342" />
The 95% confidence interval suggests Rexthor's dog could also be a cat, or possibly a teapot.
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End of explanation
"""


example_n = np.random.randint(0, Y.shape[0])

Utils.describe_example_boston_dataset(X[example_n])

print("\n\nPredicted price: {:2.2f} Real value: {:2.2f}".format(
        model.predict(X[example_n].reshape(1, -1))[0], Y[example_n]))

"""
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Congratulations!
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End of explanation
"""


iris_dataset = datasets.load_iris()

print("Features: " + str(iris_dataset.feature_names))
print("Classes: " + str(iris_dataset.target_names))

X = iris_dataset.data
y = iris_dataset.target



# Load it to a DF
idx = np.random.permutation(150)
y = y[idx]
X = X[idx]

labels = np.reshape(y,
                    (y.shape[0], 1))
df = pd.DataFrame(data=np.concatenate((X, labels), axis=1),
                 columns=iris_dataset.feature_names + ['Class'])
df.head(10)

df.describe()

# Let's take a peak at the data:
plt.figure(figsize=(8,8))
colors = "bry"
for i, color in zip([0, 1, 2], colors):
    idx = np.where(y == i)
    plt.scatter(X[idx, 0], X[idx, 1], c=color, cmap=plt.cm.Paired)
    
plt.text(5.25, 2.20, "Versicolor", fontsize=14)
plt.text(7, 3.5, "Virginica", fontsize=14)
plt.text(4.5, 3.75, "Setosa", fontsize=14)

plt.title("The 3 different Iris species", fontsize=18, 
          fontweight='bold')    
plt.xlabel(iris_dataset.feature_names[0], fontsize=14)
plt.ylabel(iris_dataset.feature_names[1], fontsize=14)

plt.show()

# We will focus identifying only the Iris Setosa
plt.figure(figsize=(8,8))
colors = "br"

idx = np.where(y == 0) # Give me the indices of the Iris Setosa examples

plt.scatter(X[idx, 0], X[idx, 1], c='b', cmap=plt.cm.Paired)
plt.text(4.5, 3.75, "Setosa", fontsize=14)

idx = np.where(y != 0) # where it's not Iris Setosa 
plt.scatter(X[idx, 0], X[idx, 1], c='r', cmap=plt.cm.Paired)
plt.text(7.0, 2.5, "Others", fontsize=14)


plt.title("Scatter plot of Iris Setosa and the others Iris",
          fontsize=18, fontweight='bold')  
plt.xlabel(iris_dataset.feature_names[0], fontsize=14)
plt.ylabel(iris_dataset.feature_names[1], fontsize=14)
plt.show()

"""
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Distinguishing Species of Iris plants:
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<img src="https://c1.staticflickr.com/1/402/31713322286_8c85f8b6b6_z.jpg" title="Iris flower" width="512" height="342" />
Source: Big Cypress National Preserve
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End of explanation
"""


# We only care about whether each flower is a 
#     Iris Setosa and we are looking only at two of their features

X = iris_dataset.data
y = iris_dataset.target

new_y = y == 0

model = LogisticRegression(random_state=42, verbose=0)

model.fit(X[:,0:2], new_y)

accuracy = model.score(X[:,0:2], new_y)

print("Accuracy: {:0.3f}%".format(accuracy*100))


from Utils import predict_mesh

# Let's take a look at what our model is doing

# First plot the examples
plt.figure(figsize=(8,8))
colors = "br"

idx = np.where(y == 0)
plt.scatter(X[idx, 0], X[idx, 1], c='b', cmap=plt.cm.Paired)
plt.text(4.5, 3.75, "Setosa", fontsize=14)

idx = np.where(y != 0)
plt.scatter(X[idx, 0], X[idx, 1], c='r', cmap=plt.cm.Paired)
plt.text(7.0, 2.5, "Others", fontsize=14)

(xx, yy, Z) = predict_mesh(X, model)
plt.contour(xx, yy, Z, cmap=plt.cm.Paired)


plt.title("Decision Boundary", fontsize=18, fontweight='bold')   
plt.xlabel(iris_dataset.feature_names[0], fontsize=14)
plt.ylabel(iris_dataset.feature_names[1], fontsize=14)
plt.show()

"""
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Second model  Logistic Regression
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<img src="2HardProblems.png" title="Tweet" width="400" />
End of explanation
"""


def sigmoid(x):
    return 1 / (1 + np.exp(-x))

x = np.arange(-20, 20, 0.001)
y = sigmoid(x)

plt.figure(figsize=(10,5))
plt.plot(x, y)
plt.title("Sigmoid Function", fontsize=14)
plt.show()

"""
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Linear Regression and Logistic Regression
So how do these models work?
Let's start with linear regression:
$$ \hat{y} = w_0 + w_1.x_1 + w_2.x_2 + w_3.x_3$$ 
Adding a $x_0=1$ we get
$$ \hat{y} = w^T \cdot x $$
For each variable we have a weight, an "importance", and the linear combination of the weights and features results in our estimated value $\hat{y}$.
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What about the weights?
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Questions?
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Logistic Regression
Same model + Classifier function
Model:
$$ \hat{y} = w^T \cdot x $$
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Model + classification:
$$ \hat{y} = g(w^T \cdot x) $$
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Sigmoid function
$$g(z) = \frac{1}{1 + e^{-z}}$$
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End of explanation
"""


# Read the data file and drop the collumns we don't care about:
whisky_dataframe = pd.read_csv(
    filepath_or_buffer="whiskies.csv", header=0, sep=',',
    index_col=1)
whisky_dataframe.drop(['RowID', 'Postcode', ' Latitude',
                       ' Longitude'], inplace=True, axis=1)
whisky_dataframe.head(10)

"""
Explanation: <br></br>
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Now to a real world example!
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Scotch!
<img src="https://c1.staticflickr.com/7/6184/6105844311_dc4c31b8b7_b.jpg" title="Scotch whiskies" width="716" height="403" />
Source: Damien Pollet
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First look at the data
Data from: Whisky Classified
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"""


whisky_dataframe.describe()

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End of explanation
"""


Utils.plot_whisky_histograms(whisky_dataframe)

Utils.plot_whiky_body_correlation(whisky_dataframe)

"""
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End of explanation
"""


Utils.plot_1d_random_data(0.5, 30)

Utils.plot_2d_random_data(0.5, 30)

"""
Explanation: <br></br>
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Let's do a Detour
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The Curse of Dimentionality
 More features != Better Results 
_ When the dimensionality increases, the volume of the space increases so fast that the available data become sparse._
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End of explanation
"""


random_data_1 =np.random.multivariate_normal(
    mean= [0, 0], cov=[[5, 5], [0, 0.5]], size=100)

random_data_2 =np.random.multivariate_normal(
    mean= [6, 6], cov=[[5, 5], [0, 0.5]], size=100)

random_data = np.concatenate([random_data_1, random_data_2], axis=0)
random_labels = np.concatenate([np.ones((100,1)),np.zeros((100,1))], axis=0)

fig = plt.figure(figsize=(8, 8))

plt.scatter(random_data[:, 0], random_data[:, 1], c=random_labels, cmap=cmap_light)
#plt.scatter(random_data_2[:, 0], random_data_2[:, 1], c='r')

plt.plot([-5, 10], [-5, 10], 'r--')
plt.plot([5, 0], [0, 5], 'g--')

plt.xlim((-7, 14))
plt.ylim((-7, 14))
plt.title('Random Data with Principal Components', fontsize=16)

plt.xlabel('Random Dimension 1', fontsize=14)
plt.ylabel('Random Dimension 2', fontsize=14)

plt.show()

pca = PCA(n_components=2)
pca.fit(random_data)
transformed_data = pca.fit_transform(random_data)

plt.figure(figsize=(8,6))
plt.scatter(transformed_data[:,0], transformed_data[:,1],
            c=random_labels, cmap=cmap_light)
plt.plot([-10, 10], [0, 0], 'r--')
plt.xlim((-10, 10))
plt.ylim((-5, 5))
plt.title('Transformed Random Data', fontsize=16)
plt.xlabel('Random Dimension 1', fontsize=14)
plt.ylabel('Random Dimension 2', fontsize=14)

plt.show()

pca = PCA(n_components=1)
pca.fit(random_data)
transformed_data = pca.fit_transform(random_data)

plt.figure(figsize=(8,5))
plt.scatter(transformed_data[:,0], np.zeros((200,1)),
            c=random_labels, cmap=cmap_light)
plt.plot([-10, 10], [0, 0], 'r--')
plt.xlim((-10, 10))
plt.ylim((-5, 5))
plt.title('Transformed Random Data', fontsize=16)
plt.xlabel('Random Dimension 1', fontsize=14)

plt.show()

print("% of variance explained by PCA: {:0.1f}% \
        ".format(
        pca.explained_variance_ratio_[0]*100))

"""
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Feature selection and extraction
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PCA
End of explanation
"""


# Adapted from: http://scikit-learn.org/stable/auto_examples/linear_model/plot_polynomial_interpolation.html 
# Author: Mathieu Blondel
#         Jake Vanderplas
# License: BSD 3 clause

def f(x, noise=False):
    """ function to approximate by polynomial interpolation"""
    if(noise):
        return np.sin(x) + np.random.randn(x.shape[0])/4
    return np.sin(x)

space_size = 6

# generate points used to plot
x_plot = np.linspace(-space_size, space_size, 100)

# generate points and keep a subset of them
x = np.linspace(-space_size, space_size, 100)
rng = np.random.RandomState(42)
rng.shuffle(x)
x = np.sort(x[:10])
y = f(x, True)

# create matrix versions of these arrays
X = x[:, np.newaxis]
X_plot = x_plot[:, np.newaxis]

colors = ['teal', 'yellowgreen', 'gold', 'blue']
lw = 2

fig = plt.figure(figsize=(12,12))
    

for count, degree in enumerate([1, 3, 6, 10]):
    ax = fig.add_subplot(2, 2, count+1)
    ax.plot(x_plot, f(x_plot), color='cornflowerblue', linewidth=lw,
         label="ground truth")
    ax.scatter(x, y, color='navy', s=30, marker='o', label="training points")
    model = make_pipeline(PolynomialFeatures(degree), Ridge())
    model.fit(X, y)
    y_plot = model.predict(X_plot)
    ax.plot(x_plot, y_plot, color=colors[count], linewidth=lw,
             label="degree %d" % degree)

    ax.legend(loc='lower left')
    ax.set_ylim((-5, 5))
plt.show()

"""
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Model complexity and overfitting
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End of explanation
"""


whisky_data = whisky_dataframe.values

pca = PCA(n_components=2, whiten=True) 

# Here whiten means centering the data around 0, 
# which is needed so that PCA works correctly
transformed_data = pca.fit_transform(whisky_data)

print("% of variance explained by each component: \
       \n 1st {:0.1f}% \
       \n 2nd {:0.1f}% \
        ".format(
        pca.explained_variance_ratio_[0]*100, 
        pca.explained_variance_ratio_[1]*100))


fig = plt.figure(figsize=(8,6))
plt.scatter(x = transformed_data[:,0], y=transformed_data[:,1])

plt.xlim((-3, 5))
plt.ylim((-3, 5))

plt.title('Transformed Whisky Data', fontsize=16)
plt.xlabel('Principal Component 1', fontsize=14)
plt.ylabel('Principal Component 2', fontsize=14)

plt.show()

"""
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Back to Scotch!
Let's apply what we learned to our dataset
End of explanation
"""


labels = whisky_dataframe['Tobacco']
whisky_data = whisky_dataframe.drop('Tobacco', axis=1).values


print("Percentage of Positive Labels: {:.2f}%".format(
        np.sum(labels)/len(labels)*100))

"""
Explanation: <br></br>
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Predicting whether it has Tobacco taste
End of explanation
"""


pca = PCA(n_components=2, whiten=True) 
# Here whiten means centering the data around 0, 
# which is neededso that PCA works correctly
transformed_data = pca.fit_transform(whisky_data)


train_data, test_data, train_labels, test_labels = train_test_split(
    transformed_data, labels, test_size=0.30, random_state=0)

# Without Class weights
classf = LogisticRegression()

# With Class weights
class_weight={0:1, 1: 12}
classf = LogisticRegression(class_weight=class_weight)

classf.fit(train_data, train_labels)

accuracy = classf.score(train_data, train_labels)

print("\n\nTraining Accuracy:\t {:0.3f}%\n\n".format(accuracy*100))

accuracy = classf.score(test_data, test_labels)

print("Test Accuracy:\t\t {:0.3f}%\n\n".format(accuracy*100))


"""
Explanation: <br></br>
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Unbalanced dataset
End of explanation
"""


print("\tTraining \n")
predicted_labels = classf.predict(train_data)
cm = confusion_matrix(train_labels, predicted_labels)
Utils.print_cm(cm)

print("\n\tTesting \n")

predicted_labels = classf.predict(test_data)
cm = confusion_matrix(test_labels, predicted_labels)
Utils.print_cm(cm)

"""
Explanation: Confusion Matrix
End of explanation
"""


class_weight={0:1, 1: 12}

classf = LogisticRegression(random_state=42, 
                            class_weight=class_weight)
#classf = LogisticRegression(random_state=42)

# Select parameters to use in Cross-Validation
classf_cv = classf
data_cv = transformed_data
N_CV = 10

# Cross Validation
t0 = time()
scores = cross_val_score(classf_cv, data_cv, labels, cv = N_CV)
print("Scores: ")
for i, score in enumerate(scores):
    print( '\t' + str(i) + ':\t' + str(score)) 
print("Accuracy: %0.3f (+/- %0.3f)" % (scores.mean(), scores.std() * 2))
print("\nCross val done in %0.3fs." % (time() - t0))

"""
Explanation: <br></br>
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Cross Validation
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<img src="https://upload.wikimedia.org/wikipedia/commons/1/1c/K-fold_cross_validation_EN.jpg" alt="K-fold cross validation EN.jpg">
End of explanation
"""


random_data = np.random.randn(100, 2)
random_labels = np.random.randint(0,2,100)

fig = plt.figure(figsize=(8,8))

plt.scatter(random_data[:, 0], random_data[:, 1],
            c=random_labels, cmap=cmap_bold)

plt.xlabel('Random Dimension 1', fontsize=14)
plt.ylabel('Random Dimension 2', fontsize=14)

plt.show()

"""
Explanation: <br></br>
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Resources:
Online Courses:

Machine Learning by Andrew Ng

Books:

Master Algorithm
Pattern Recognition and Machine Learning

Articles:

A few useful things to know about Machine Learning

Tutorials:

Kaggle: Predicting Survival rate on the Titanic
Scikit-Learn Tutorials

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Unsupervised Learning
### Clustering: K -means
TODO: Explain with toy data set, add video / GIF?
Clustering Scotch:
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Model Complexity and Overfitting
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End of explanation
"""


clf = KNeighborsClassifier(n_neighbors=1)
clf = KNeighborsClassifier(n_neighbors=10)

clf.fit(random_data, random_labels)

print("Accuracy: {:0.3f}%".format(
        clf.score(random_data, random_labels)*100))


(xx, yy, Z) = Utils.predict_mesh(random_data, clf, h=0.01)

fig = plt.figure(figsize=(8,8))
plt.xlabel('Random Dimension 1', fontsize=14)
plt.ylabel('Random Dimension 2', fontsize=14)
plt.pcolormesh(xx, yy, Z, cmap=cmap_light)

plt.scatter(random_data[:, 0], random_data[:, 1],
            c=random_labels, cmap=cmap_bold)

plt.show()

random_labels = np.concatenate([np.ones((50,)), np.zeros((50,))])

random_data = np.concatenate([
    np.add(np.multiply(np.random.randn(50, 2), 
                       np.array([0.7, 1.5])), np.array([3, 1])),
    np.multiply(np.random.randn(50, 2), 
                np.array([0.5, 3]))
    ]) 


fig = plt.figure(figsize=(8, 8))

plt.scatter(random_data[:, 0], random_data[:, 1], 
            c=random_labels, cmap=cmap_bold)
plt.xlim((-4, 8))
plt.ylim((-6, 6))
plt.xlabel('Random Dimension 1', fontsize=14)
plt.ylabel('Random Dimension 2', fontsize=14)

plt.show()

"""
Explanation: K-Nearest Neighbors Classifier
End of explanation
"""


whisky_data = pd.read_csv(
    filepath_or_buffer="Meta-Critic Whisky Database – Selfbuilts Whisky Analysis.csv")
whisky_data.describe()
whisky_data.head()


"""
Explanation: There is so much more
This can not even be considered scraping the surface. Go ahead and experiment it's a very interesting field, and there are tons of information and places to learn from!
End of explanation
"""


