# %matplotlib inline

import pandas as pd
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.neighbors import NearestNeighbors,KNeighborsClassifier

# UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. plt.show()
import matplotlib
# matplotlib.use('TkAgg')
import matplotlib.pyplot as plt

from matplotlib.backends.backend_pdf import PdfPages
pp = PdfPages('ch7.pdf')


# mower_df = pd.read_csv('/opt/notebooks/Codes/codes/RidingMowers.csv')
mower_df = pd.read_csv('RidingMowers.csv')

mower_df['Number'] = mower_df.index + 1
trainData, validData = train_test_split(mower_df, test_size=0.4, random_state=26)
## new household
newHousehold = pd.DataFrame([{'Income': 60, 'Lot_Size': 20}])
## scatter plot
def plotDataset(ax, data, showLabel=True, **kwargs):
   subset = data.loc[data['Ownership']=='Owner']
   ax.scatter(subset.Income, subset.Lot_Size, marker='o',
   label='Owner' if showLabel else None, color='C1',
   **kwargs)
   subset = data.loc[data['Ownership']=='Nonowner']
   ax.scatter(subset.Income, subset.Lot_Size, marker='D',
   label='Nonowner' if showLabel else None, color='C0',
   **kwargs)
   plt.xlabel('Income') # set x-axis label
   plt.ylabel('Lot_Size') # set y-axis label
   for _, row in data.iterrows():
     ax.annotate(row.Number, (row.Income + 2, row.Lot_Size))
fig, ax = plt.subplots()
plotDataset(ax, trainData)
plotDataset(ax, validData, showLabel=False, facecolors='none')
ax.scatter(newHousehold.Income, newHousehold.Lot_Size,
marker='*',
label='New household', color='black', s=150)
plt.xlabel('Income'); plt.ylabel('Lot_Size')
ax.set_xlim(40, 115)
handles, labels = ax.get_legend_handles_labels()
ax.legend(handles, labels, loc=4)
pp.savefig()
plt.show()


predictors = ['Income', 'Number']
# outcome =
# initialize normalized training, validation, and complete data frames
# use the training data to learn the transformation.
scaler = preprocessing.StandardScaler()
scaler.fit(trainData[['Income', 'Lot_Size']]) # Note use of array of column names
# Transform the full dataset
mowerNorm = pd.concat([pd.DataFrame(scaler.transform(mower_df[['Income', 'Lot_Size']]),
columns=['zIncome',   'zLot_Size']),
mower_df[['Ownership', 'Number']]],    axis=1)
trainNorm = mowerNorm.iloc[trainData.index]
validNorm = mowerNorm.iloc[validData.index]
newHouseholdNorm = pd.DataFrame(scaler.transform(newHousehold),  columns=['zIncome', 'zLot_Size'])

# use NearestNeighbors from scikit-learn to compute knn
from sklearn.neighbors import NearestNeighbors
knn = NearestNeighbors(n_neighbors=3)  # knn ###################################################
knn.fit(trainNorm.iloc[:, 0:2])
distances, indices = knn.kneighbors(newHouseholdNorm)
# indices is a list of lists, we are only interested in the first  element
print(trainNorm.iloc[indices[0], :])


train_X = trainNorm[['zIncome', 'zLot_Size']]
train_y = trainNorm['Ownership']
valid_X = validNorm[['zIncome', 'zLot_Size']]
valid_y = validNorm['Ownership']
# Train a classifier for different values of k
results = []
for k in range(1, 15):
   knn = KNeighborsClassifier(n_neighbors=k).fit(train_X, train_y)
   results.append({  'k': k,  'accuracy': accuracy_score(valid_y, knn.predict(valid_X)) })
# Convert results to a pandas data frame
results = pd.DataFrame(results)
print(results)


# Retrain with full dataset
mower_X = mowerNorm[['zIncome', 'zLot_Size']]
mower_y = mowerNorm['Ownership']
knn = KNeighborsClassifier(n_neighbors=4).fit(mower_X, mower_y)
distances, indices = knn.kneighbors(newHouseholdNorm)
print(knn.predict(newHouseholdNorm))
print('Distances',distances)
print('Indices', indices)
print(mowerNorm.iloc[indices[0], :])