Blubook Bulldozer

Shows the usage of aiking library on a kaggle dataset

Import public packages

import fastcore
import pandas as pd
import pathlib
from fastcore.all import *
from fastcore.imports import *
import os
import sys
import sklearn
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import cross_val_score, cross_val_predict, KFold, train_test_split
from sklearn.metrics import ConfusionMatrixDisplay, accuracy_score, plot_confusion_matrix, confusion_matrix
from IPython.display import display
from sklearn.pipeline import make_pipeline
from sklearn.ensemble import RandomForestRegressor
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler, OneHotEncoder

Import private packages

is_kaggle = 'kaggle_secrets' in sys.modules
if is_kaggle:
    from kaggle_secrets import UserSecretsClient
    user_secrets = UserSecretsClient()
    os.environ['KAGGLE_USERNAME'] = user_secrets.get_secret("kaggle_username")
    if not os.environ['KAGGLE_USERNAME']: raise Exception("Please insert your Kaggle username and key into Kaggle secrets")
    os.environ['KAGGLE_KEY'] = user_secrets.get_secret("kaggle_key")
    github_pat = user_secrets.get_secret("GITHUB_PAT")
    !pip install -Uqq git+https://{github_pat}@github.com/Rahuketu86/aiking
else:
    from aiking.data.external import *
    path = untar_data("kaggle_competitions::bluebook-bulldozer-remix"); 
    print(path.ls())
[Path('/AIKING_HOME/data/bluebook-bulldozer-remix/bluebook-bulldozer-remix.zip'), Path('/AIKING_HOME/data/bluebook-bulldozer-remix/Data Dictionary.xlsx'), Path('/AIKING_HOME/data/bluebook-bulldozer-remix/Train'), Path('/AIKING_HOME/data/bluebook-bulldozer-remix/TrainingData'), Path('/AIKING_HOME/data/bluebook-bulldozer-remix/Valid')]
from aiking.ml.structured import *

Read the Dataset

data_dir = pathlib.Path(os.getenv('DATA_DIR', "/kaggle/input")); 
path = data_dir/"bluebook-bulldozer-remix"
path.ls()
(#5) [Path('/kaggle/input/bluebook-bulldozer-remix/bluebook-bulldozer-remix.zip'),Path('/kaggle/input/bluebook-bulldozer-remix/Data Dictionary.xlsx'),Path('/kaggle/input/bluebook-bulldozer-remix/Train'),Path('/kaggle/input/bluebook-bulldozer-remix/TrainingData'),Path('/kaggle/input/bluebook-bulldozer-remix/Valid')]
df_train = pd.read_csv(path/"Train/Train.csv", low_memory=False,parse_dates=['saledate'], infer_datetime_format=True); df_train.head()
df_test = pd.read_csv(path/"Valid/Valid.csv", parse_dates=['saledate'], infer_datetime_format=True); df_test.head()
SalesID MachineID ModelID datasource auctioneerID YearMade MachineHoursCurrentMeter UsageBand saledate fiModelDesc ... Undercarriage_Pad_Width Stick_Length Thumb Pattern_Changer Grouser_Type Backhoe_Mounting Blade_Type Travel_Controls Differential_Type Steering_Controls
0 1222837 902859 1376 121 3 1000 0.0 NaN 2012-01-05 375L ... None or Unspecified None or Unspecified None or Unspecified None or Unspecified Double NaN NaN NaN NaN NaN
1 1222839 1048320 36526 121 3 2006 4412.0 Medium 2012-01-05 TX300LC2 ... None or Unspecified 12' 4" None or Unspecified Yes Double NaN NaN NaN NaN NaN
2 1222841 999308 4587 121 3 2000 10127.0 Medium 2012-01-05 270LC ... None or Unspecified 12' 4" None or Unspecified None or Unspecified Double NaN NaN NaN NaN NaN
3 1222843 1062425 1954 121 3 1000 4682.0 Low 2012-01-05 892DLC ... None or Unspecified None or Unspecified None or Unspecified None or Unspecified Double NaN NaN NaN NaN NaN
4 1222845 1032841 4701 121 3 2002 8150.0 Medium 2012-01-04 544H ... NaN NaN NaN NaN NaN NaN NaN NaN Standard Conventional

5 rows × 52 columns

Modelling

Define categories ordering

max_n_cat = 0
cat_dict = get_cat_dict(df_train, max_n_cat=max_n_cat)
display_cat(cat_dict)
0 1
0 UsageBand [High, Low, Medium]
1 fiModelDesc [100C, 104, 1066, 1066E, 1080, 1080B, 1088, 1088CK, 1088LT, 1088TTL, 10B, 10C, 10DG, 110, 1105, 110S, 110TLB, 110Z, 110Z-2, 112, 112E, 112F, 115, 1150, 1150B, 1150BLGP, 1150C, 1150D, 1150E, 1150ELT, 1150G, 1150GLGP, 1150GLT, 1150H, 1150HLGP, 1150HLT, 1150HWT, 1150K, 1150KLGPSERIES3, 1150KWT, 1150KXLT, 1150KXLTIII, 115SRDZ, 115Z, 115ZIII, 115ZIV, 115ZIV-2, 115ZV, 116, 1166, 118, 1187C, 1188, 1188LC, 1188P, 118B, 118C, 11B, 11C, 12, 120, 120B, 120C, 120CLC, 120D, 120E, 120G, 120H, 120HNA, 120LC, 120M , 125, 125A, 125B, 125C, 125CKBNA, 1280, 1280B, 1288, 12E, 12F, 12G, 12H, 12HNA, 12JX, 130, 1300, 1300D, 130G, 130LC, 130LX, 1340XL, 135, 135C, 135CRTS, 135H, 135HNA, 135MSR SPIN ACE, 135SR, 135SRLC, ...]
2 fiBaseModel [10, 100, 104, 1066, 1080, 1088, 10DG, 11, 110, 1105, 112, 115, 1150, 116, 1166, 118, 1187, 1188, 12, 120, 125, 1280, 1288, 130, 1300, 1340, 135, 137, 14, 140, 1400, 143, 1450, 15, 150, 1500, 153, 155, 1550, 16, 160, 1600, 163, 165, 1650, 166, 17, 170, 1700, 1737, 1740, 175, 1750, 1760XL, 17ZTS, 18, 1800, 1818, 1825, 1830, 1835, 1838, 1840, 1845, 185, 1850, 190, 1900, 198, 20, 200, 2000, 2022, 2026, 2040, 2042, 2044, 205, 2050, 2054, 2060, 2060XL, 2064, 2066, 2070, 2074, 2076, 208, 2086, 2095, 2099, 21, 210, 2105, 2109, 211, 212, 213, 213LC, 214, ...]
3 fiSecondaryDesc [ MSR SPIN ACE, #NAME?, -2, -3, -5, -5L, -6, -7, 0.7, 1, 2, 3, 5, 7, A, AA, AB, AG, AW, AX, B, B , BEC, BL, BLGP, BLGPPS, BZ, BZD, C, C , CE, CH, CK, CKB, CL, CLR, CM, CR, CS, CX, D, DC, DL, DT, DX, DXT, DZ, E, EG, EL, ESS, EST, EW, EX, F, FR, G, GT, H, H , H90, HAG, HD, HDS, HDSL, HF, HL, HLGP, HLS, HX, HZ, IV, J, JX, K, L, LC, LC7A, LC7LR, LCD, LCH, LCLR, LCM, LD, LE, LGP, LR, LS, LT, LX, M, M , MC, MR, MRX, MSR, MSR SPIN ACE, MT, MU, MXT, ...]
4 fiModelSeries [ III, #NAME?, -1, -1.50E+01, -11, -12, -15, -16, -17, -18, -1B, -1C, -1L, -2, -20, -21, -21A, -2A, -2C, -2E, -2LC, -2N, -3, -3C, -3EO, -3H, -3L, -3LC, -3LK, -3M, -3MC, -3PT, -4, -5, -5A, -5E, -5F, -5H, -5L, -5LC, -6, -6A, -6B, -6C, -6E, -6K, -6LC, -6LK, -7, -7B, -7E, -7K, -8, -8E, 1, 12, 14FT, 15, 16, 17, 18, 1970, 2, 20, 21KomStat, 21KomStatII, 22, 2B, 2T, 3, 3A, 3C, 4, 5, 5N, 6, 6.00E+00, 6F, 6L, 6LE, 6LK, 7, 7.00E+00, 7A, 7L, 8, A, AWS, D, D7, E, EX, Elite, FASTRRACK, G, GALEO, H, II, III, IV, ...]
5 fiModelDescriptor [ 14FT, LGP, SUPER, XLT, XT, ZX, (BLADE RUNNER), 1, 2, 2.00E+00, 2N, 3, 3.00E+00, 3C, 3L, 3NO, 4WD, 4x4x4, 5, 6, 6K, 7, 7.00E+00, 7A, 8, A, AE0, AVANCE, B, BE, C, CK, CR, CRSB, CUSTOM, DA, DELUXE, DHP, DINGO, DLL, DT, DW, E, ESL, G, GALEO, H, H5, HD, HF, HSD, HT, High Lift, HighLift, II, III, IT, IV, K, K3, K5, KA, KBNA, L, LC, LC8, LCH, LCR, LCRTS, LE, LGP, LGPVP, LITRONIC, LK, LL, LM, LN, LR, LRC, LRR, LS, LT, LU, LX, LongReach, M, MC, ME, MH, N, NLC, NSUC, P, PLUS, PRO, RR, RTS, S, SA, SB, ...]
6 ProductSize [Compact, Large, Large / Medium, Medium, Mini, Small]
7 fiProductClassDesc [Backhoe Loader - 0.0 to 14.0 Ft Standard Digging Depth, Backhoe Loader - 14.0 to 15.0 Ft Standard Digging Depth, Backhoe Loader - 15.0 to 16.0 Ft Standard Digging Depth, Backhoe Loader - 16.0 + Ft Standard Digging Depth, Backhoe Loader - Unidentified, Hydraulic Excavator, Track - 0.0 to 2.0 Metric Tons, Hydraulic Excavator, Track - 11.0 to 12.0 Metric Tons, Hydraulic Excavator, Track - 12.0 to 14.0 Metric Tons, Hydraulic Excavator, Track - 14.0 to 16.0 Metric Tons, Hydraulic Excavator, Track - 150.0 to 300.0 Metric Tons, Hydraulic Excavator, Track - 16.0 to 19.0 Metric Tons, Hydraulic Excavator, Track - 19.0 to 21.0 Metric Tons, Hydraulic Excavator, Track - 2.0 to 3.0 Metric Tons, Hydraulic Excavator, Track - 21.0 to 24.0 Metric Tons, Hydraulic Excavator, Track - 24.0 to 28.0 Metric Tons, Hydraulic Excavator, Track - 28.0 to 33.0 Metric Tons, Hydraulic Excavator, Track - 3.0 to 4.0 Metric Tons, Hydraulic Excavator, Track - 300.0 + Metric Tons, Hydraulic Excavator, Track - 33.0 to 40.0 Metric Tons, Hydraulic Excavator, Track - 4.0 to 5.0 Metric Tons, Hydraulic Excavator, Track - 4.0 to 6.0 Metric Tons, Hydraulic Excavator, Track - 40.0 to 50.0 Metric Tons, Hydraulic Excavator, Track - 5.0 to 6.0 Metric Tons, Hydraulic Excavator, Track - 50.0 to 66.0 Metric Tons, Hydraulic Excavator, Track - 6.0 to 8.0 Metric Tons, Hydraulic Excavator, Track - 66.0 to 90.0 Metric Tons, Hydraulic Excavator, Track - 8.0 to 11.0 Metric Tons, Hydraulic Excavator, Track - 90.0 to 150.0 Metric Tons, Hydraulic Excavator, Track - Unidentified, Hydraulic Excavator, Track - Unidentified (Compact Construction), Motorgrader - 130.0 to 145.0 Horsepower, Motorgrader - 145.0 to 170.0 Horsepower, Motorgrader - 170.0 to 200.0 Horsepower, Motorgrader - 200.0 + Horsepower, Motorgrader - 45.0 to 130.0 Horsepower, Motorgrader - Unidentified, Skid Steer Loader - 0.0 to 701.0 Lb Operating Capacity, Skid Steer Loader - 1251.0 to 1351.0 Lb Operating Capacity, Skid Steer Loader - 1351.0 to 1601.0 Lb Operating Capacity, Skid Steer Loader - 1601.0 to 1751.0 Lb Operating Capacity, Skid Steer Loader - 1751.0 to 2201.0 Lb Operating Capacity, Skid Steer Loader - 2201.0 to 2701.0 Lb Operating Capacity, Skid Steer Loader - 2701.0+ Lb Operating Capacity, Skid Steer Loader - 701.0 to 976.0 Lb Operating Capacity, Skid Steer Loader - 976.0 to 1251.0 Lb Operating Capacity, Skid Steer Loader - Unidentified, Track Type Tractor, Dozer - 105.0 to 130.0 Horsepower, Track Type Tractor, Dozer - 130.0 to 160.0 Horsepower, Track Type Tractor, Dozer - 160.0 to 190.0 Horsepower, Track Type Tractor, Dozer - 190.0 to 260.0 Horsepower, Track Type Tractor, Dozer - 20.0 to 75.0 Horsepower, Track Type Tractor, Dozer - 260.0 + Horsepower, Track Type Tractor, Dozer - 75.0 to 85.0 Horsepower, Track Type Tractor, Dozer - 85.0 to 105.0 Horsepower, Track Type Tractor, Dozer - Unidentified, Wheel Loader - 0.0 to 40.0 Horsepower, Wheel Loader - 100.0 to 110.0 Horsepower, Wheel Loader - 1000.0 + Horsepower, Wheel Loader - 110.0 to 120.0 Horsepower, Wheel Loader - 120.0 to 135.0 Horsepower, Wheel Loader - 135.0 to 150.0 Horsepower, Wheel Loader - 150.0 to 175.0 Horsepower, Wheel Loader - 175.0 to 200.0 Horsepower, Wheel Loader - 200.0 to 225.0 Horsepower, Wheel Loader - 225.0 to 250.0 Horsepower, Wheel Loader - 250.0 to 275.0 Horsepower, Wheel Loader - 275.0 to 350.0 Horsepower, Wheel Loader - 350.0 to 500.0 Horsepower, Wheel Loader - 40.0 to 60.0 Horsepower, Wheel Loader - 500.0 to 1000.0 Horsepower, Wheel Loader - 60.0 to 80.0 Horsepower, Wheel Loader - 80.0 to 90.0 Horsepower, Wheel Loader - 90.0 to 100.0 Horsepower, Wheel Loader - Unidentified]
8 state [Alabama, Alaska, Arizona, Arkansas, California, Colorado, Connecticut, Delaware, Florida, Georgia, Hawaii, Idaho, Illinois, Indiana, Iowa, Kansas, Kentucky, Louisiana, Maine, Maryland, Massachusetts, Michigan, Minnesota, Mississippi, Missouri, Montana, Nebraska, Nevada, New Hampshire, New Jersey, New Mexico, New York, North Carolina, North Dakota, Ohio, Oklahoma, Oregon, Pennsylvania, Puerto Rico, Rhode Island, South Carolina, South Dakota, Tennessee, Texas, Unspecified, Utah, Vermont, Virginia, Washington, Washington DC, West Virginia, Wisconsin, Wyoming]
9 ProductGroup [BL, MG, SSL, TEX, TTT, WL]
10 ProductGroupDesc [Backhoe Loaders, Motor Graders, Skid Steer Loaders, Track Excavators, Track Type Tractors, Wheel Loader]
11 Drive_System [All Wheel Drive, Four Wheel Drive, No, Two Wheel Drive]
12 Enclosure [EROPS, EROPS AC, EROPS w AC, NO ROPS, None or Unspecified, OROPS]
13 Forks [None or Unspecified, Yes]
14 Pad_Type [Grouser, None or Unspecified, Reversible, Street]
15 Ride_Control [No, None or Unspecified, Yes]
16 Stick [Extended, Standard]
17 Transmission [AutoShift, Autoshift, Direct Drive, Hydrostatic, None or Unspecified, Powershift, Powershuttle, Standard]
18 Turbocharged [None or Unspecified, Yes]
19 Blade_Extension [None or Unspecified, Yes]
20 Blade_Width [12', 13', 14', 16', <12', None or Unspecified]
21 Enclosure_Type [High Profile, Low Profile, None or Unspecified]
22 Engine_Horsepower [No, Variable]
23 Hydraulics [2 Valve, 3 Valve, 4 Valve, Auxiliary, Base + 1 Function, Base + 2 Function, Base + 3 Function, Base + 4 Function, Base + 5 Function, Base + 6 Function, None or Unspecified, Standard]
24 Pushblock [None or Unspecified, Yes]
25 Ripper [Multi Shank, None or Unspecified, Single Shank, Yes]
26 Scarifier [None or Unspecified, Yes]
27 Tip_Control [None or Unspecified, Sideshift & Tip, Tip]
28 Tire_Size [10 inch, 10", 13", 14", 15.5, 15.5", 17.5, 17.5", 20.5, 20.5", 23.1", 23.5, 23.5", 26.5, 29.5, 7.0", None or Unspecified]
29 Coupler [Hydraulic, Manual, None or Unspecified]
30 Coupler_System [None or Unspecified, Yes]
31 Grouser_Tracks [None or Unspecified, Yes]
32 Hydraulics_Flow [High Flow, None or Unspecified, Standard]
33 Track_Type [Rubber, Steel]
34 Undercarriage_Pad_Width [14 inch, 15 inch, 16 inch, 18 inch, 20 inch, 22 inch, 24 inch, 25 inch, 26 inch, 27 inch, 28 inch, 30 inch, 31 inch, 31.5 inch, 32 inch, 33 inch, 34 inch, 36 inch, None or Unspecified]
35 Stick_Length [10' 10", 10' 2", 10' 6", 11' 0", 11' 10", 12' 10", 12' 4", 12' 8", 13' 10", 13' 7", 13' 9", 14' 1", 15' 4", 15' 9", 19' 8", 24' 3", 6' 3", 7' 10", 8' 10", 8' 2", 8' 4", 8' 6", 9' 10", 9' 2", 9' 5", 9' 6", 9' 7", 9' 8", None or Unspecified]
36 Thumb [Hydraulic, Manual, None or Unspecified]
37 Pattern_Changer [No, None or Unspecified, Yes]
38 Grouser_Type [Double, Single, Triple]
39 Backhoe_Mounting [None or Unspecified, Yes]
40 Blade_Type [Angle, Coal, Landfill, No, None or Unspecified, PAT, Semi U, Straight, U, VPAT]
41 Travel_Controls [1 Speed, 2 Pedal, Differential Steer, Finger Tip, Lever, None or Unspecified, Pedal]
42 Differential_Type [Limited Slip, Locking, No Spin, Standard]
43 Steering_Controls [Command Control, Conventional, Four Wheel Standard, No, Wheel] 
updates = {
    'UsageBand':['High', 'Medium', 'Low'],
    'Blade_Width':[ "16'", "14'", "13'","12'", "<12'", "None or Unspecified"],
    'Grouser_Type':['Triple', 'Double', 'Single'],
    'ProductSize':['Large','Large / Medium', 'Medium', 'Compact', 'Small', 'Mini']
}

cat_dict.update(updates)
filter_dict = {k:v for k,v in cat_dict.items() if k in updates}
display_cat(filter_dict)
0 1
0 UsageBand [High, Medium, Low]
1 ProductSize [Large, Large / Medium, Medium, Compact, Small, Mini]
2 Blade_Width [16', 14', 13', 12', <12', None or Unspecified]
3 Grouser_Type [Triple, Double, Single]

Define Validation Set

Code 
def range_data(df_train, df_test, names=['Train', 'Test'], datecol='saledate'):
    return pd.DataFrame([
        {'Name':names[0], 'Start':df_train[datecol].min(), 'End': df_train[datecol].max(), 'Interval':df_train[datecol].max() -df_train[datecol].min(), 'Size': len(df_train) },
        {'Name':names[1], 'Start':df_test[datecol].min(), 'End': df_test[datecol].max(), 'Interval':df_test[datecol].max() -df_test[datecol].min(), 'Size': len(df_test) }]).set_index('Name')
range_data(df_train, df_test)
Start End Interval Size
Name
Train 1989-01-17 2011-12-30 8382 days 401125
Test 2012-01-01 2012-04-28 118 days 11573

Test data starts at the end of train date with about 4 months of data (covering February with 28/29 days). We need to split our internal validation set in a similar way. We will take 4 months / 120 days of data as validation set

validation_date_start = (df_train[['saledate']].max() - pd.Timedelta(value=120, unit='D')).values[0] # Last 4 months data for internal validation
df_model, df_valid = df_train[df_train['saledate'] < validation_date_start], df_train[df_train['saledate']>= validation_date_start]
range_data(df_model, df_valid, names=['Model', 'Valid'])
Start End Interval Size
Name
Model 1989-01-17 2011-08-31 8261 days 390213
Valid 2011-09-01 2011-12-30 120 days 10912

Define Pipeline

def get_model_pipeline(max_n_cat=0, 
                       cat_dict=None, 
                       scale_dict={'class': StandardScaler},
                       cat_num_dict={'class':NumericalEncoder,'categories':None},
                       cat_dummy_dict={'class':OneHotEncoder,'handle_unknown':'ignore'},
                       imputer_dict={'class':SimpleImputer, 'strategy':'median'},
                      ):
    layer_spec_default = (get_default_feature_def, 
                      {
                          'skip_flds':None, 
                          'ignored_flds':None, 
                          'max_n_cat':max_n_cat, 
                          'na_exclude_cols':[],
                          'scale_var_num':True,
                          'scale_var_cat':False,
                          'scale_dict':scale_dict,
                          'cat_num_dict':cat_num_dict,
                          'cat_dummy_dict':cat_dummy_dict,
                          'imputer_dict':imputer_dict,
                          'include_time_cols':True,
                          'keep_dt_cols':False,
                          'cat_dict':cat_dict
                      }
                     )

    layer_specs = [layer_spec_default]
    proc = Proc(layer_specs=layer_specs); #proc.fit_transform(X)
    model = RandomForestRegressor(n_jobs=-1)
    pipeline = make_pipeline(proc, model); pipeline
    return pipeline
pipeline = get_model_pipeline(cat_dict); pipeline
Pipeline(steps=[('proc', <aiking.ml.structured.Proc object>),
                ('randomforestregressor', RandomForestRegressor(n_jobs=-1))])

Train on Partial Data

max_n_cat = 0

def get_xy(df, col='SalePrice'): return df.drop([col], axis=1), np.log(df[col])
    
df = df_model.sample(frac=0.04)
X, y = get_xy(df)
X_model, y_model = get_xy(df_model)
X_valid, y_valid = get_xy(df_valid)

pipeline = get_model_pipeline(max_n_cat,cat_dict)
pipeline.fit(X, y)
Pipeline(steps=[('proc', <aiking.ml.structured.Proc object>),
                ('randomforestregressor', RandomForestRegressor(n_jobs=-1))])
get_score(pipeline, X, y, X_valid, y_valid, scorers=get_scorer_dict())
Training Validation
Metric
r2 0.978088 0.821789
neg_root_mean_squared_error -0.102343 -0.305869
explained_variance 0.978089 0.825985
neg_median_absolute_error -0.055909 -0.181245
neg_mean_absolute_percentage_error -0.007455 -0.022704 
get_score(pipeline, X_model, y_model, X_valid, y_valid, scorers=get_scorer_dict())
Training Validation
Metric
r2 0.853482 0.821789
neg_root_mean_squared_error -0.265110 -0.305869
explained_variance 0.853483 0.825985
neg_median_absolute_error -0.144865 -0.181245
neg_mean_absolute_percentage_error -0.019361 -0.022704

This gives an indication of estimate of msle around .26 to .30[Really 0.304 from validation estimate]

Cross validation estimate

df_cv = timeseries_cv(df_model, 'saledate', 'SalePrice', 
                  pipeline_callback_dict={'func': get_model_pipeline, 'func_kwargs':{'max_n_cat':0, 'cat_dict':cat_dict}},
                  y_mod_func=np.log,
                  scorers = get_scorer_dict(),
                  n_train=15000, n_test=12000, n_splits=10)
df_cv
100.00% [10/10 01:06<00:00]
Training Validation set train_start train_end valid_start valid_end
Metric
r2 0.986050 0.855443 1 2008-02-26 2008-06-28 2008-06-28 2008-11-07
neg_root_mean_squared_error -0.082520 -0.262933 1 2008-02-26 2008-06-28 2008-06-28 2008-11-07
explained_variance 0.986051 0.863314 1 2008-02-26 2008-06-28 2008-06-28 2008-11-07
neg_median_absolute_error -0.037583 -0.148024 1 2008-02-26 2008-06-28 2008-06-28 2008-11-07
neg_mean_absolute_percentage_error -0.005600 -0.019542 1 2008-02-26 2008-06-28 2008-06-28 2008-11-07
r2 0.984919 0.839832 2 2008-02-26 2008-11-07 2008-11-07 2009-02-16
neg_root_mean_squared_error -0.085098 -0.280481 2 2008-02-26 2008-11-07 2008-11-07 2009-02-16
explained_variance 0.984919 0.865230 2 2008-02-26 2008-11-07 2008-11-07 2009-02-16
neg_median_absolute_error -0.040576 -0.167759 2 2008-02-26 2008-11-07 2008-11-07 2009-02-16
neg_mean_absolute_percentage_error -0.005859 -0.021296 2 2008-02-26 2008-11-07 2008-11-07 2009-02-16
r2 0.985811 0.855938 3 2008-02-26 2009-02-16 2009-02-16 2009-05-05
neg_root_mean_squared_error -0.083452 -0.268049 3 2008-02-26 2009-02-16 2009-02-16 2009-05-05
explained_variance 0.985812 0.870222 3 2008-02-26 2009-02-16 2009-02-16 2009-05-05
neg_median_absolute_error -0.039221 -0.161301 3 2008-02-26 2009-02-16 2009-02-16 2009-05-05
neg_mean_absolute_percentage_error -0.005723 -0.020529 3 2008-02-26 2009-02-16 2009-02-16 2009-05-05
r2 0.986149 0.859721 4 2008-02-29 2009-05-05 2009-05-05 2009-08-19
neg_root_mean_squared_error -0.082841 -0.263684 4 2008-02-29 2009-05-05 2009-05-05 2009-08-19
explained_variance 0.986149 0.860370 4 2008-02-29 2009-05-05 2009-05-05 2009-08-19
neg_median_absolute_error -0.039276 -0.152460 4 2008-02-29 2009-05-05 2009-05-05 2009-08-19
neg_mean_absolute_percentage_error -0.005729 -0.019758 4 2008-02-29 2009-05-05 2009-05-05 2009-08-19
r2 0.985700 0.864896 5 2008-03-05 2009-08-19 2009-08-19 2009-12-04
neg_root_mean_squared_error -0.084180 -0.254076 5 2008-03-05 2009-08-19 2009-08-19 2009-12-04
explained_variance 0.985700 0.864995 5 2008-03-05 2009-08-19 2009-08-19 2009-12-04
neg_median_absolute_error -0.040860 -0.144788 5 2008-03-05 2009-08-19 2009-08-19 2009-12-04
neg_mean_absolute_percentage_error -0.005935 -0.018973 5 2008-03-05 2009-08-19 2009-08-19 2009-12-04
r2 0.984630 0.847664 6 2008-05-01 2009-12-04 2009-12-04 2010-03-30
neg_root_mean_squared_error -0.086090 -0.272201 6 2008-05-01 2009-12-04 2009-12-04 2010-03-30
explained_variance 0.984633 0.855422 6 2008-05-01 2009-12-04 2009-12-04 2010-03-30
neg_median_absolute_error -0.040860 -0.155152 6 2008-05-01 2009-12-04 2009-12-04 2010-03-30
neg_mean_absolute_percentage_error -0.005967 -0.019876 6 2008-05-01 2009-12-04 2009-12-04 2010-03-30
r2 0.985190 0.843297 7 2008-05-15 2010-03-30 2010-03-30 2010-08-24
neg_root_mean_squared_error -0.084891 -0.278687 7 2008-05-15 2010-03-30 2010-03-30 2010-08-24
explained_variance 0.985192 0.844513 7 2008-05-15 2010-03-30 2010-03-30 2010-08-24
neg_median_absolute_error -0.039577 -0.151982 7 2008-05-15 2010-03-30 2010-03-30 2010-08-24
neg_mean_absolute_percentage_error -0.005821 -0.020361 7 2008-05-15 2010-03-30 2010-03-30 2010-08-24
r2 0.984002 0.871116 8 2008-11-12 2010-08-24 2010-08-24 2011-01-26
neg_root_mean_squared_error -0.088833 -0.253561 8 2008-11-12 2010-08-24 2010-08-24 2011-01-26
explained_variance 0.984003 0.871649 8 2008-11-12 2010-08-24 2010-08-24 2011-01-26
neg_median_absolute_error -0.043196 -0.143973 8 2008-11-12 2010-08-24 2010-08-24 2011-01-26
neg_mean_absolute_percentage_error -0.006185 -0.018764 8 2008-11-12 2010-08-24 2010-08-24 2011-01-26
r2 0.985709 0.859266 9 2009-02-16 2011-01-26 2011-01-26 2011-04-28
neg_root_mean_squared_error -0.084228 -0.265014 9 2009-02-16 2011-01-26 2011-01-26 2011-04-28
explained_variance 0.985709 0.867493 9 2009-02-16 2011-01-26 2011-01-26 2011-04-28
neg_median_absolute_error -0.041602 -0.146238 9 2009-02-16 2011-01-26 2011-01-26 2011-04-28
neg_mean_absolute_percentage_error -0.005938 -0.019083 9 2009-02-16 2011-01-26 2011-01-26 2011-04-28
r2 0.986866 0.860514 10 2009-05-06 2011-04-28 2011-04-28 2011-08-31
neg_root_mean_squared_error -0.081017 -0.263815 10 2009-05-06 2011-04-28 2011-04-28 2011-08-31
explained_variance 0.986871 0.860803 10 2009-05-06 2011-04-28 2011-04-28 2011-08-31
neg_median_absolute_error -0.037374 -0.151020 10 2009-05-06 2011-04-28 2011-04-28 2011-08-31
neg_mean_absolute_percentage_error -0.005516 -0.019471 10 2009-05-06 2011-04-28 2011-04-28 2011-08-31 
df_cv.loc['neg_root_mean_squared_error'][['train_end', 'Training', 'Validation']].set_index('train_end').plot()
<AxesSubplot:xlabel='train_end'>

df_cv.loc['neg_root_mean_squared_error'][[ 'Validation']].mean()
Validation   -0.26625
dtype: float64

Tuning

Number of points to select for training to improve validation

This requires little bit of playing with how many points to choose for training

n = 8000
df_model_train = df_model.sort_values(by='saledate').iloc[-n:]
df_model_train.sample(frac=1)
X_model_train , y_model_train = get_xy(df_model_train)
pipeline = get_model_pipeline(max_n_cat, cat_dict)
pipeline.fit(X_model_train, y_model_train)
score_df = get_score(pipeline, X_model_train, y_model_train, X_valid, y_valid, scorers=get_scorer_dict())
score_df['n'] = n
score_df
Training Validation n
Metric
r2 0.986471 0.848635 8000
neg_root_mean_squared_error -0.081511 -0.281891 8000
explained_variance 0.986471 0.849255 8000
neg_median_absolute_error -0.038784 -0.157271 8000
neg_mean_absolute_percentage_error -0.005624 -0.020596 8000 
np.linspace(20000, 5000,16)
array([20000., 19000., 18000., 17000., 16000., 15000., 14000., 13000.,
       12000., 11000., 10000.,  9000.,  8000.,  7000.,  6000.,  5000.])
def get_score_for_n(n=8000):
    df_model_train = df_model.sort_values(by='saledate').iloc[-n:]
    df_model_train.sample(frac=1)
    X_model_train , y_model_train = get_xy(df_model_train)
    pipeline = get_model_pipeline(max_n_cat, cat_dict)
    pipeline.fit(X_model_train, y_model_train)
    score_df = get_score(pipeline, X_model_train, y_model_train, X_valid, y_valid, scorers=get_scorer_dict())
    score_df['n'] = n
    return score_df
scores  = pd.concat([get_score_for_n(n=int(n)) for n in np.linspace(20000, 5000,16)]); scores
Training Validation n
Metric
r2 0.987612 0.869907 20000
neg_root_mean_squared_error -0.078250 -0.261333 20000
explained_variance 0.987613 0.870216 20000
neg_median_absolute_error -0.037010 -0.147194 20000
neg_mean_absolute_percentage_error -0.005343 -0.019104 20000
... ... ... ...
r2 0.985790 0.833147 5000
neg_root_mean_squared_error -0.083945 -0.295961 5000
explained_variance 0.985790 0.834022 5000
neg_median_absolute_error -0.038053 -0.166622 5000
neg_mean_absolute_percentage_error -0.005691 -0.021768 5000

80 rows × 3 columns

scores.loc['neg_root_mean_squared_error']
Training Validation n
Metric
neg_root_mean_squared_error -0.078250 -0.261333 20000
neg_root_mean_squared_error -0.078630 -0.260586 19000
neg_root_mean_squared_error -0.079291 -0.261858 18000
neg_root_mean_squared_error -0.080207 -0.262133 17000
neg_root_mean_squared_error -0.080376 -0.265427 16000
neg_root_mean_squared_error -0.081357 -0.265461 15000
neg_root_mean_squared_error -0.081260 -0.267446 14000
neg_root_mean_squared_error -0.080965 -0.270179 13000
neg_root_mean_squared_error -0.080303 -0.270147 12000
neg_root_mean_squared_error -0.081419 -0.273167 11000
neg_root_mean_squared_error -0.081806 -0.276547 10000
neg_root_mean_squared_error -0.081851 -0.279289 9000
neg_root_mean_squared_error -0.082139 -0.281973 8000
neg_root_mean_squared_error -0.081183 -0.284795 7000
neg_root_mean_squared_error -0.081215 -0.285585 6000
neg_root_mean_squared_error -0.083945 -0.295961 5000

Conclusion is we can try 2 models

  • Add 19000 points from df_model with df_valid
  • Add df_model with df_valid and take last 19000 points

Predictions

df_model_train = df_model.sort_values(by='saledate').iloc[-19000:]
df_sel_train = pd.concat([df_model_train, df_valid])
df_sel_train.sample(frac=1)
X_sel_train, y_sel_train = get_xy(df_sel_train)

pipeline = get_model_pipeline(max_n_cat, cat_dict)
pipeline.fit(X_sel_train, y_sel_train)
Pipeline(steps=[('proc', <aiking.ml.structured.Proc object>),
                ('randomforestregressor', RandomForestRegressor(n_jobs=-1))])
plt.hist(np.exp(y_sel_train))
(array([10186.,  8235.,  4265.,  2565.,  1941.,  1222.,   922.,   350.,
          166.,    60.]),
 array([  4750.,  18475.,  32200.,  45925.,  59650.,  73375.,  87100.,
        100825., 114550., 128275., 142000.]),
 <BarContainer object of 10 artists>)

plt.hist(np.exp(pipeline.predict(df_test)))
(array([3914., 2885., 1674., 1086.,  809.,  575.,  409.,  141.,   48.,
          32.]),
 array([  5632.98698052,  17963.68207264,  30294.37716476,  42625.07225689,
         54955.76734901,  67286.46244114,  79617.15753326,  91947.85262538,
        104278.54771751, 116609.24280963, 128939.93790175]),
 <BarContainer object of 10 artists>)

df_submission = pd.DataFrame()
df_submission['Id'] = df_test['SalesID']
df_submission['Predicted'] = np.exp(pipeline.predict(df_test))
df_submission.to_csv('submission.csv', index=False)
if not is_kaggle:
    import kaggle
    from aiking.integrations.kaggle import push2kaggle
    # kaggle.api.competition_submit_cli("submission.csv", "Submission from local machine", competition="bluebook-bulldozer-remix")
    push2kaggle("00_index.ipynb")