# Code you have previously used to load data
import pandas as pd
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor


# Path of the file to read
iowa_file_path = '../input/home-data-for-ml-course/train.csv'

home_data = pd.read_csv(iowa_file_path)
# Create target object and call it y
y = home_data.SalePrice
# Create X
features = ['LotArea', 'YearBuilt', '1stFlrSF', '2ndFlrSF', 'FullBath', 'BedroomAbvGr', 'TotRmsAbvGrd']
X = home_data[features]

# Split into validation and training data
train_X, val_X, train_y, val_y = train_test_split(X, y, random_state=1)

# Specify Model
iowa_model = DecisionTreeRegressor(random_state=1)
# Fit Model
iowa_model.fit(train_X, train_y)

# Make validation predictions and calculate mean absolute error
val_predictions = iowa_model.predict(val_X)
val_mae = mean_absolute_error(val_predictions, val_y)
print("Validation MAE: {:,.0f}".format(val_mae))

# Set up code checking
from learntools.core import binder
binder.bind(globals())
from learntools.machine_learning.ex5 import *
print("\nSetup complete")

"""
Explanation: Recap
You've built your first model, and now it's time to optimize the size of the tree to make better predictions. Run this cell to set up your coding environment where the previous step left off.
End of explanation
"""


def get_mae(max_leaf_nodes, train_X, val_X, train_y, val_y):
    model = DecisionTreeRegressor(max_leaf_nodes=max_leaf_nodes, random_state=0)
    model.fit(train_X, train_y)
    preds_val = model.predict(val_X)
    mae = mean_absolute_error(val_y, preds_val)
    return(mae)

"""
Explanation: Exercises
You could write the function get_mae yourself. For now, we'll supply it. This is the same function you read about in the previous lesson. Just run the cell below.
End of explanation
"""


candidate_max_leaf_nodes = [5, 25, 50, 100, 250, 500]
# Write loop to find the ideal tree size from candidate_max_leaf_nodes
_

# Store the best value of max_leaf_nodes (it will be either 5, 25, 50, 100, 250 or 500)
best_tree_size = ____

# Check your answer
step_1.check()

# The lines below will show you a hint or the solution.
# step_1.hint() 
# step_1.solution()

#%%RM_IF(PROD)%%
candidate_max_leaf_nodes = [5, 25, 50, 100, 250, 500]
scores = {leaf_size: get_mae(leaf_size, train_X, val_X, train_y, val_y) for leaf_size in candidate_max_leaf_nodes}

best_tree_size = min(scores, key=scores.get)
step_1.assert_check_passed()

#%%RM_IF(PROD)%%

step_1.solution()

"""
Explanation: Step 1: Compare Different Tree Sizes
Write a loop that tries the following values for max_leaf_nodes from a set of possible values.
Call the get_mae function on each value of max_leaf_nodes. Store the output in some way that allows you to select the value of max_leaf_nodes that gives the most accurate model on your data.
End of explanation
"""


# Fill in argument to make optimal size and uncomment
# final_model = DecisionTreeRegressor(____)

# fit the final model and uncomment the next two lines
# final_model.fit(____, ____)

# Check your answer
step_2.check()

# step_2.hint()
# step_2.solution()

#%%RM_IF(PROD)%%

# Fit the model with best_tree_size. Fill in argument to make optimal size
final_model = DecisionTreeRegressor(max_leaf_nodes = best_tree_size, random_state=2)

# fit the final model
final_model.fit(X, y)

step_2.assert_check_passed()

"""
Explanation: Step 2: Fit Model Using All Data
You know the best tree size. If you were going to deploy this model in practice, you would make it even more accurate by using all of the data and keeping that tree size.  That is, you don't need to hold out the validation data now that you've made all your modeling decisions.
End of explanation
"""


