In [ ]:

    

%load_ext autoreload
%autoreload 2


    

In [ ]:

    

%matplotlib inline

from fastai.imports import *
from fastai.torch_imports import *
from fastai.dataset import *
from fastai.learner import *
from fastai.structured import *
from fastai.column_data import *


    

In [ ]:

    

dep = 'SalePrice'
PATH = "data/bulldozers/"
df_raw = pd.read_feather('tmp/bulldozers-raw')
keep_cols = list(np.load('tmp/keep_cols.npy'))


    

In [ ]:

    

df_raw.loc[df_raw.YearMade<1950, 'YearMade'] = 1950
df_raw['age'] = df_raw.saleYear-df_raw.YearMade
df_raw = df_raw[keep_cols+['age', dep]].copy()
df_indep = df_raw.drop(dep,axis=1)

n_valid = 12000
n_trn = len(df_raw)-n_valid


    

In [ ]:

    

cat_flds = [n for n in df_indep.columns if df_raw[n].nunique()<n_trn/50]
' '.join(cat_flds)


    

    
Out[ ]:





'YearMade Coupler_System ProductSize fiProductClassDesc ModelID saleElapsed fiSecondaryDesc Enclosure fiModelDesc Hydraulics_Flow fiModelDescriptor Hydraulics Drive_System ProductGroupDesc ProductGroup state saleDay Track_Type saleDayofyear Stick_Length age'

In [ ]:

    

for o in ['saleElapsed', 'saleDayofyear', 'saleDay', 'age', 'YearMade']: cat_flds.remove(o)
[n for n in df_indep.drop(cat_flds,axis=1).columns if not is_numeric_dtype(df_raw[n])]


    

    
Out[ ]:





[]

In [ ]:

    

for n in cat_flds: df_raw[n] = df_raw[n].astype('category').cat.as_ordered()

cont_flds = [n for n in df_indep.columns if n not in cat_flds]
' '.join(cont_flds)


    

    
Out[ ]:





'YearMade saleElapsed SalesID MachineID saleDay saleDayofyear age'

In [ ]:

    

df_raw = df_raw[cat_flds+cont_flds+[dep]]
df, y, nas, mapper = proc_df(df_raw, 'SalePrice', do_scale=True)

val_idx = list(range(n_trn, len(df)))


    

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md = ColumnarModelData.from_data_frame(PATH, val_idx, df, y, cat_flds=cat_flds, bs=64)


    

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df.head()


    

    
Out[ ]:







  

    

      

      
Coupler_System
      
ProductSize
      
fiProductClassDesc
      
ModelID
      
fiSecondaryDesc
      
Enclosure
      
fiModelDesc
      
Hydraulics_Flow
      
fiModelDescriptor
      
Hydraulics
      
...
      
state
      
Track_Type
      
Stick_Length
      
YearMade
      
saleElapsed
      
SalesID
      
MachineID
      
saleDay
      
saleDayofyear
      
age
    
  
  

    

      
0
      
0
      
0
      
59
      
644
      
41
      
3
      
950
      
0
      
0
      
1
      
...
      
1
      
0
      
0
      
0.913196
      
0.397377
      
-0.858580
      
-0.496185
      
-0.013101
      
1.352092
      
-0.828814
    
    

      
1
      
0
      
4
      
62
      
11
      
55
      
3
      
1725
      
0
      
0
      
1
      
...
      
33
      
0
      
0
      
0.405756
      
-0.061496
      
-0.858578
      
-2.494936
      
1.173518
      
-0.907472
      
-0.430749
    
    

      
2
      
1
      
0
      
39
      
1542
      
0
      
6
      
331
      
3
      
0
      
4
      
...
      
32
      
0
      
0
      
0.722906
      
-0.075286
      
-0.858577
      
-1.775759
      
1.173518
      
-1.187503
      
-0.762470
    
    

      
3
      
0
      
6
      
8
      
110
      
0
      
3
      
3674
      
0
      
0
      
1
      
...
      
44
      
0
      
0
      
0.722906
      
1.179600
      
-0.858574
      
-0.434096
      
0.342885
      
-0.395690
      
-0.298060
    
    

      
4
      
1
      
0
      
40
      
3540
      
0
      
1
      
4208
      
3
      
0
      
4
      
...
      
32
      
0
      
0
      
1.103486
      
0.863382
      
-0.858572
      
-0.364020
      
0.817532
      
0.231967
      
-0.828814
    
  

5 rows × 23 columns

In [ ]:

    

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


    

In [ ]:

    

emb_c = {n: len(c.cat.categories)+1 for n,c in df_raw[cat_flds].items()}
emb_c


    

    
Out[ ]:





{'Coupler_System': 3,
 'Drive_System': 5,
 'Enclosure': 7,
 'Hydraulics': 13,
 'Hydraulics_Flow': 4,
 'ModelID': 5219,
 'ProductGroup': 7,
 'ProductGroupDesc': 7,
 'ProductSize': 7,
 'Stick_Length': 30,
 'Track_Type': 3,
 'fiModelDesc': 5000,
 'fiModelDescriptor': 140,
 'fiProductClassDesc': 75,
 'fiSecondaryDesc': 176,
 'state': 54}

In [ ]:

    

emb_szs = [(c, min(50, (c+1)//2)) for _,c in emb_c.items()]
metrics=[rmse]


    

In [ ]:

    

y_range=(0,np.max(y)*1.2)


    

In [ ]:

    

m = md.get_learner(emb_szs, len(cont_flds), 0.05, 1, [500,250], [0.5,0.05],
                   y_range=y_range, use_bn=True)


    

In [ ]:

    

m.lr_find()


    

    






 
 










    




 63%|██████▎   | 3812/6081 [00:28<00:14, 154.49it/s, loss=0.202] 

In [ ]:

    

m.sched.plot(1300)


    

    













    




 63%|██████▎   | 3812/6081 [00:40<00:23, 95.21it/s, loss=0.314]

In [ ]:

    

lr=1e-3; wd=1e-7


    

In [ ]:

    

m.fit(lr, 2, wd, cycle_len=1, cycle_mult=2)


    

    






 
 










    




[ 0.       0.06207  0.09731]                                     
[ 1.       0.06048  0.07684]                                     
[ 2.       0.05326  0.06389]                                     

In [ ]:

    

m.fit(lr, 2, wd, cycle_len=2, cycle_mult=2)


    

    






 
 










    




[ 0.       0.05471  0.0523 ]                                     
[ 1.       0.04767  0.0512 ]                                     
[ 2.       0.05249  0.05747]                                     
[ 3.       0.04643  0.05393]                                     
[ 4.       0.04984  0.04934]                                     
[ 5.       0.04277  0.04869]                                     

In [ ]:

    

math.sqrt(0.0487)


    

    
Out[ ]:





0.22068076490713912