#!/usr/bin/env python3

# -*- coding: utf-8 -*-

"""

Created on Mon May 28 16:35:35 2018

@author: gazal

"""

# Pandas is used for data manipulation

import pandas as pd

# Use numpy to convert to arrays

import numpy as np

# Using Skicit-learn to split data into training and testing sets

from sklearn.model_selection import train_test_split

# Import the model we are using

from sklearn.ensemble import RandomForestRegressor

# Import tools needed for visualization

from sklearn.tree import export_graphviz

import pydot

data_path = "/home/gazal/Documents/LapsationRFM/Data/fab_india_customers.csv"

df = pd.read_csv(data_path)

# Read in data and display first 5 rows

features = df.drop(["id","avg_latency","ATV"], axis=1)

print(features.head(5))

print('The shape of our features is:', features.shape)

# Descriptive statistics for each column

print(features.describe())

# One-hot encode the data using pandas get_dummies

dummies = pd.get_dummies(features)

features_f = pd.concat([features, dummies], axis=1)

# Display the first 5 rows of the last 12 columns

print(features_f.head(5))

# Labels are the values we want to predict

labels = np.array(df['avg_latency'])

# Remove the labels from the features

# axis 1 refers to the columns

features= features_f.drop('gender', axis = 1)

# Saving feature names for later use

feature_list = list(features.columns)

# Convert to numpy array

features = np.array(features)

# Split the data into training and testing sets

train_features, test_features, train_labels, test_labels = train_test_split(features, labels, test_size = 0.25, random_state = 42)

print('Training Features Shape:', train_features.shape)

print('Training Labels Shape:', train_labels.shape)

print('Testing Features Shape:', test_features.shape)

print('Testing Labels Shape:', test_labels.shape)

"""

# Instantiate model with 1000 decision trees

rf = RandomForestRegressor(n_estimators = 1000, random_state = 42)

# Train the model on training data

rf.fit(train_features, train_labels);

# Use the forest's predict method on the test data

predictions = rf.predict(test_features)

# Calculate the absolute errors

errors = abs(predictions - test_labels)

# Print out the mean absolute error (mae)

print('Mean Absolute Error:', round(np.mean(errors), 2), 'degrees.')

# Use the forest's predict method on the test data

predictions = rf.predict(test_features)

# Calculate the absolute errors

errors = abs(predictions - test_labels)

# Print out the mean absolute error (mae)

print('Mean Absolute Error:', round(np.mean(errors), 2), 'degrees.')

# Pull out one tree from the forest

tree = rf.estimators_[5]

"""

"""

# Import tools needed for visualization

from sklearn.tree import export_graphviz

import pydot

# Pull out one tree from the forest

tree = rf.estimators_[5]

# Export the image to a dot file

export_graphviz(tree, out_file = 'tree.dot', feature_names = feature_list, rounded = True, precision = 1)

# Use dot file to create a graph

(graph, ) = pydot.graph_from_dot_file('tree.dot')

# Write graph to a png file

graph.write_png('/home/gazal/Documents/LapsationRFM/Data/tree.png')

"""