In [1]:

    

%load_ext autoreload
%autoreload 2
%matplotlib inline


from fastai.imports import *
from fastai.structured import *

from pandas_summary import DataFrameSummary
from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
from IPython.display import display

from sklearn import metrics


    

In [2]:

    

PATH = "data/bulldozers/"


    

In [7]:

    

# df_raw = pd.read_csv(f'{PATH}Train.csv', low_memory=False,
#                      parse_dates=["saledate"])
# df_raw.SalePrice = np.log(df_raw.SalePrice)
# os.makedirs('tmp', exist_ok=True)
# df_raw.to_feather('tmp/df_raw.feather')


    

In [3]:

    

df_raw = pd.read_feather('tmp/df_raw.feather')
add_datepart(df_raw, 'saledate')
train_cats(df_raw)
df, y, nas = proc_df(df_raw, 'SalePrice')


    

In [4]:

    

def split_vals(a, n): return a[:n].copy(), a[n:].copy()

n_valid = 12000 # same as Kaggle's test set size
n_trn = len(df) - n_valid
raw_train, raw_valid = split_vals(df_raw, n_trn)
X_train, X_valid = split_vals(df, n_trn)
y_train, y_valid = split_vals(y, n_trn)

# X_train.shape, y_train.shape, X_valid.shape

def rmse(x,y): return math.sqrt(((x-y)**2).mean())

def print_score(m):
    res = [rmse(m.predict(X_train), y_train), rmse(m.predict(X_valid), y_valid), 
                m.score(X_train, y_train), m.score(X_valid, y_valid)]
    if hasattr(m, 'oob_score_'): res.append(m.oob_score_)
    print(res)


    

In [5]:

    

df_trn, y_trn, nas = proc_df(df_raw, 'SalePrice', subset=30000, na_dict=nas)
X_train, _ = split_vals(df_trn, 20000)
y_train, _ = split_vals(y_trn, 20000)


    

In [6]:

    

m = RandomForestRegressor(n_estimators=1, max_depth=3, bootstrap=False, n_jobs=-1)
m.fit(X_train, y_train)
print_score(m)


    

    




[0.5254209267350565, 0.5809499119884205, 0.3940282308089076, 0.3972664664069727]

In [8]:

    

draw_tree(m.estimators_[0], df_trn, precision=3)


    

In [ ]: