# Split the dataset
  # Feature scaling

  # Apply Kernel
  from sklearn.decomposition import KernelPCA
  # kpca = KernelPCA(kernel="rbf", fit_inverse_transform=True, gamma=10)
  # X_kpca = kpca.fit_transform(X)
  # X_back = kpca.inverse_transform(X_kpca)
  # pca = PCA()
  # X_pca = pca.fit_transform(X)
  kpca = KernelPCA(n_components = 2, kernel = 'rbf')
  X_train = kpca.fit_transform(X_train)
  X_test = kpca.transform(X_test)

  # Fit Logistic Regression to the Training set

  # Split the dataset
  # Feature scaling

  from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA

  lda = LDA(n_components = 2)
  # X_r2 = lda.fit(X, y).transform(X)
  X_train = lda.fit_transform(X_train, y_train)
  X_test = lda.transform(X_test)

  # Fit Logistic Regression to the Training set

  # Split the dataset
  # Feature scaling

  from sklearn.decomposition import PCA

  pca = PCA(n_components=2)
  # X_r = pca.fit(X).transform(X)
  X = pca.fit_transform(X_train)
  X = pca.transform(X_test)
  explained_variance = pca.explained_variance_ratio_

  # Percentage of variance explained for each components
  print('explained variance ratio (first two components): %s'
        % str(pca.explained_variance_ratio_))

  # Fit Logistic Regression to the Training set

import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import seaborn as sns
import statsmodels.api as sm

from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.linear_model import LogisticRegression
from sklearn.feature_selection import RFE, SelectKBest, f_regression
from sklearn.model_selection import train_test_split
from sklearn.ensemble import ExtraTreesClassifier, RandomForestClassifier
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from sklearn.datasets import make_classification

import tensorflow as tf

%matplotlib inline

from sklearn.datasets import load_breast_cancer

cancer = load_breast_cancer()
print(cancer.keys())
print('target names ', cancer['target_names'])
# print(cancer['DESCR'])
df = pd.DataFrame(cancer['data'],columns=cancer['feature_names'])
features_for_pca = df.columns.shape[0]
df['target'] = cancer['target']
print(df.groupby('target')['target'].count())

scaler = StandardScaler()
scaler.fit(df.drop('target', axis = 1))
scaled_data = scaler.transform(df.drop('target', axis = 1))

# Replace spaces
column_names  = []
for name in df.columns:
    column_names.append(name.replace(" ", "_"))

df.columns = column_names
# print(df.columns)

df.head()

# https://www.analyticsvidhya.com/blog/2016/03/practical-guide-principal-component-analysis-python/

pca_full = PCA(n_components=features_for_pca)
pca_full.fit(scaled_data)

# Amount of variance for each component
var = pca_full.explained_variance_ratio_

# Cumulative sum
cumsum = np.cumsum(np.round(pca_full.explained_variance_ratio_, decimals=4)*100)

# Find the number of pca components that account for 95% (~10)
plt.figure(figsize=(12, 4))

# plt.subplot(nrows=1, ncols=3, nplt=1)
plt.subplot(121)
plt.plot(var, 'b-')
plt.title('Variance')
plt.xlabel('Components')
plt.ylabel('% Explained Variance')
plt.grid()

plt.subplot(122)
plt.plot(cumsum, 'r-')
plt.title('Cumulative Sum')
plt.xlabel('Components')
plt.ylabel('Sum % Explained Variance')
plt.grid()