'''Partially based on grid_plus_classifier script:
https://www.kaggle.com/svpons/facebook-v-predicting-check-ins/grid-knn/
https://www.kaggle.com/overfit/facebook-v-predicting-check-ins/grid-knn/
https://www.kaggle.com/apapiu/facebook-v-predicting-check-ins/grid-knn-mins
'''
import numpy as np
import pandas as pd
import time
from sklearn.preprocessing import LabelEncoder
from sklearn.neighbors import KNeighborsClassifier

pd.options.mode.chained_assignment = None  # default='warn'

# Classification inside one grid cell.
def calculate_distance(distances):
    return distances ** -2

def process_one_cell(df_cell_train, df_cell_test, fw, th):
    
    #Working on df_train
    place_counts = df_cell_train.place_id.value_counts()
    mask = (place_counts[df_cell_train.place_id.values] >= th).values
    df_cell_train = df_cell_train.loc[mask]
    
    #Working on df_test
    row_ids = df_cell_test.index
    
    #Feature engineering on x and y
    df_cell_train.loc[:,'x'] *= fw[0]
    df_cell_train.loc[:,'y'] *= fw[1]
    df_cell_test.loc[:,'x'] *= fw[0]
    df_cell_test.loc[:,'y'] *= fw[1]
    
    #Preparing data
    le = LabelEncoder()
    y = le.fit_transform(df_cell_train.place_id.values)
    X = df_cell_train.drop(['place_id'], axis=1).values
    X_test = df_cell_test.values

    #Applying the classifier
    clf = KNeighborsClassifier(n_neighbors=26, weights=calculate_distance, 
                               metric='manhattan', n_jobs = 2)
    clf.fit(X, y)
    y_pred = clf.predict_proba(X_test)
    pred_labels = le.inverse_transform(np.argsort(y_pred, axis=1)[:,::-1][:,:3]) 
    
    return pred_labels, row_ids
   
def process_grid(df_train, df_test, size, x_step, y_step, x_border_augment, y_border_augment, fw, th):
    """
    Iterates over all grid cells, aggregates the results and makes the
    submission.
    """ 
    preds = np.zeros((df_test.shape[0], 3), dtype=int)

    for i in range((int)(size/x_step)):
        start_time_row = time.time()
        x_min = x_step * i
        x_max = x_step * (i+1)
        x_min = round(x_min, 4)
        x_max = round(x_max, 4) 
        if x_max == size:
            x_max = x_max + 0.001
            
        df_col_train = df_train[(df_train['x'] >= x_min-x_border_augment) & (df_train['x'] < x_max+x_border_augment)]
        df_col_test = df_test[(df_test['x'] >= x_min) & (df_test['x'] < x_max)]

        for j in range((int)(size/y_step)):
            y_min = y_step * j
            y_max = y_step * (j+1)
            y_min = round(y_min, 4)
            y_max = round(y_max, 4)   
            if y_max == size:
                y_max = y_max + 0.001
                
            df_cell_train = df_col_train[(df_col_train['y'] >= y_min-y_border_augment) & (df_col_train['y'] < y_max+y_border_augment)]
            df_cell_test = df_col_test[(df_col_test['y'] >= y_min) & (df_col_test['y'] < y_max)]
            
            #Applying classifier to one grid cell
            pred_labels, row_ids = process_one_cell(df_cell_train, df_cell_test, fw, th)

            #Updating predictions
            preds[row_ids] = pred_labels

        print("Row %d/%d elapsed time: %.2f seconds" % (i+1, (int)(size/x_step),(time.time() - start_time_row)))

    print('Generating submission file ...')
    #Auxiliary dataframe with the 3 best predictions for each sample
    df_aux = pd.DataFrame(preds, dtype=str, columns=['l1', 'l2', 'l3'])  
    
    #Concatenating the 3 predictions for each sample
    ds_sub = df_aux.l1.str.cat([df_aux.l2, df_aux.l3], sep=' ')
    
    #Writting to csv
    ds_sub.name = 'place_id'
    ds_sub.to_csv('sub_knn.csv', index=True, header=True, index_label='row_id')  
      
##########################################################
# Main
if __name__ == '__main__':
    
    # Input varialbles
    fw = [500., 1000., 4., 3., 2., 10., 10.] #feature weights (black magic here)
    th = 5 #Keeping place_ids with more than th samples.
    
    #Defining the size of the grid
    size = 10.0
    x_step = 0.5
    y_step = 0.5
    
    x_border_augment = 0.025    
    y_border_augment = 0.025
    
    print('Loading data ...')
    df_train = pd.read_csv('../input/train.csv',
                           usecols=['row_id','x','y','time','place_id','accuracy'], 
                           index_col = 0)
    df_test = pd.read_csv('../input/test.csv',
                          usecols=['row_id','x','y','time','accuracy'],
                          index_col = 0)
                          
    #Feature engineering

    minute = df_train.time%60
    df_train['hour'] = df_train['time']//60
    df_train['weekday'] = df_train['hour']//24
    df_train['month'] = df_train['weekday']//30
    df_train['year'] = (df_train['weekday']//365+1)*fw[5]
    df_train['hour'] = ((df_train['hour']%24+1)+minute/60.0)*fw[2]
    df_train['weekday'] = (df_train['weekday']%7+1)*fw[3]
    df_train['month'] = (df_train['month']%12+1)*fw[4]
    df_train['accuracy'] = np.log10(df_train['accuracy'])*fw[6]
    df_train.drop(['time'], axis=1, inplace=True)
    
    minute = df_test['time']%60
    df_test['hour'] = df_test['time']//60
    df_test['weekday'] = df_test['hour']//24
    df_test['month'] = df_test['weekday']//30
    df_test['year'] = (df_test['weekday']//365+1)*fw[5]
    df_test['hour'] = ((df_test['hour']%24+1)+minute/60.0)*fw[2]
    df_test['weekday'] = (df_test['weekday']%7+1)*fw[3]
    df_test['month'] = (df_test['month']%12+1)*fw[4]
    df_test['accuracy'] = np.log10(df_test['accuracy'])*fw[6]
    df_test.drop(['time'], axis=1, inplace=True)
                  
    print('Solving')
    #Solving classification problems inside each grid cell 
    process_grid(df_train, df_test, size, x_step, y_step, x_border_augment, y_border_augment, fw, th)