

In [15]:

    
%load_ext autoreload
%autoreload 2
import os, sys
sys.path.append('..')
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_context("poster")
import numpy as np
from sklearn.ensemble import RandomForestRegressor
import pandas as pd
import matplotlib as mpl
mpl.rcParams['figure.figsize'] = (11,8)
from merf.merf import MERF
from sklearn.model_selection import train_test_split, KFold
from merf.evaluator import plot_bhat, plot_training_stats









    



The autoreload extension is already loaded. To reload it, use:
  %reload_ext autoreload

Sleep Study

We evaluate the performance of MERF on a famous sleep study dataset with 180 samples and 18 clusters (with 10 samples each).



In [2]:

    
sleep_df = pd.read_csv('../data/sleepstudy.csv')



In [3]:

    
fig, ax = plt.subplots(figsize=(15,12))
for label, group in sleep_df.groupby('Subject'):
    group.plot(x='Days', y='Reaction', ax=ax, label=label)
plt.legend()
plt.grid('on')
plt.ylabel('Reaction')









    Out[3]:





Text(0,0.5,'Reaction')



In [4]:

    
sleep_df.head()



In [5]:

    
train, test = train_test_split(sleep_df, test_size=0.1, shuffle=True)
y = test['Reaction']

# Random Forest Training and Prediction
rf = RandomForestRegressor(n_estimators=100)
rf.fit(train[['Days']], train['Reaction'])
yhat_rf = rf.predict(test[['Days']])

# Mixed Effects Random Forest Training
mrf = MERF(n_estimators=100, max_iterations=20)
X_train =  train[['Days']]
Z_train = np.ones((len(X_train), 1))
clusters_train = train['Subject']
y_train = train['Reaction']
mrf.fit(X_train, Z_train, clusters_train, y_train)

# Mixed Effects Random Forest Test
X_test =  test[['Days']]
Z_test = np.ones((len(X_test), 1))
clusters_test = test['Subject']
yhat_mrf = mrf.predict(X_test, Z_test, clusters_test)

# Compute MSE
mse_rf = np.sqrt(np.sum((y - yhat_rf)**2)) / len(y)
mse_mrf = np.sqrt(np.sum((y - yhat_mrf)**2)) / len(y)

# Plot Scatter Plot of error
plt.plot(y, yhat_rf, 'o', label='RF')
plt.plot(y, yhat_mrf, 'o', label='MERF')
plt.grid('on')
plt.xlabel('y')
plt.ylabel('y_hat')

plt.legend()
plt.title('MSE RF = {:.3f}, MSE MERF = {:.3f}'.format(mse_rf, mse_mrf))









    



INFO     [merf.py:235] GLL is 1428.749557088927 at iteration 1.
INFO     [merf.py:235] GLL is 1425.5519247941138 at iteration 2.
INFO     [merf.py:235] GLL is 1427.5016491580543 at iteration 3.
INFO     [merf.py:235] GLL is 1426.223170709782 at iteration 4.
INFO     [merf.py:235] GLL is 1426.8582308869234 at iteration 5.
INFO     [merf.py:235] GLL is 1425.3316283769757 at iteration 6.
INFO     [merf.py:235] GLL is 1426.213538434854 at iteration 7.
INFO     [merf.py:235] GLL is 1427.1921105283773 at iteration 8.
INFO     [merf.py:235] GLL is 1427.2937595034323 at iteration 9.
INFO     [merf.py:235] GLL is 1426.158543998992 at iteration 10.
INFO     [merf.py:235] GLL is 1426.6317845949256 at iteration 11.
INFO     [merf.py:235] GLL is 1426.189895300844 at iteration 12.
INFO     [merf.py:235] GLL is 1427.3712529491766 at iteration 13.
INFO     [merf.py:235] GLL is 1425.8500253947238 at iteration 14.
INFO     [merf.py:235] GLL is 1425.7749213064371 at iteration 15.
INFO     [merf.py:235] GLL is 1426.029472972588 at iteration 16.
INFO     [merf.py:235] GLL is 1425.3220814077772 at iteration 17.
INFO     [merf.py:235] GLL is 1425.2637847476096 at iteration 18.
INFO     [merf.py:235] GLL is 1426.6397373324521 at iteration 19.
INFO     [merf.py:235] GLL is 1426.870469388508 at iteration 20.






    Out[5]:





Text(0.5,1,'MSE RF = 9.011, MSE MERF = 6.332')



In [6]:

    
plot_training_stats(mrf)



In [10]:

    
bb = plot_bhat(mrf, 2)









    



/Users/souravdey/.virtualenvs/merf/lib/python3.6/site-packages/ipykernel_launcher.py:1: DeprecationWarning: 
Panel is deprecated and will be removed in a future version.
The recommended way to represent these types of 3-dimensional data are with a MultiIndex on a DataFrame, via the Panel.to_frame() method
Alternatively, you can use the xarray package http://xarray.pydata.org/en/stable/.
Pandas provides a `.to_xarray()` method to help automate this conversion.

  """Entry point for launching an IPython kernel.

Radon

We evaluate the performance of MERF on the famous Minnesota radon dataset.



In [3]:

    
radon_df = pd.read_csv('../data/radon.csv')



In [4]:

    
radon_df.head()









    Out[4]:







  
    
      
      Unnamed: 0
      idnum
      state
      state2
      stfips
      zip
      region
      typebldg
      floor
      room
      ...
      pcterr
      adjwt
      dupflag
      zipflag
      cntyfips
      county
      fips
      Uppm
      county_code
      log_radon
    
  
  
    
      0
      0
      5081.0
      MN
      MN
      27.0
      55735
      5.0
      1.0
      1.0
      3.0
      ...
      9.7
      1146.499190
      1.0
      0.0
      1.0
      AITKIN
      27001.0
      0.502054
      0
      0.832909
    
    
      1
      1
      5082.0
      MN
      MN
      27.0
      55748
      5.0
      1.0
      0.0
      4.0
      ...
      14.5
      471.366223
      0.0
      0.0
      1.0
      AITKIN
      27001.0
      0.502054
      0
      0.832909
    
    
      2
      2
      5083.0
      MN
      MN
      27.0
      55748
      5.0
      1.0
      0.0
      4.0
      ...
      9.6
      433.316718
      0.0
      0.0
      1.0
      AITKIN
      27001.0
      0.502054
      0
      1.098612
    
    
      3
      3
      5084.0
      MN
      MN
      27.0
      56469
      5.0
      1.0
      0.0
      4.0
      ...
      24.3
      461.623670
      0.0
      0.0
      1.0
      AITKIN
      27001.0
      0.502054
      0
      0.095310
    
    
      4
      4
      5085.0
      MN
      MN
      27.0
      55011
      3.0
      1.0
      0.0
      4.0
      ...
      13.8
      433.316718
      0.0
      0.0
      3.0
      ANOKA
      27003.0
      0.428565
      1
      1.163151
    
  

5 rows × 30 columns



In [5]:

    
radon_df[['floor', 'Uppm', 'county', 'log_radon']].head()



In [40]:

    
max_iter = 200
cv = KFold(n_splits=5, shuffle=True)
mse_rf = []
mse_rfplus = []
mse_mrf = []
mse_mrf_uranium = []
mse_mrf_uranium_both = []

for train_idx, test_idx in cv.split(radon_df):
    # actually split the data
    train = radon_df.iloc[train_idx]
    test = radon_df.iloc[test_idx]
    # ground truth
    y = test['log_radon']
    
    # Random Forest Training and test prediction
    rf = RandomForestRegressor(n_estimators=100)
    rf.fit(train[['floor']], train['log_radon'])
    yhat_rf = rf.predict(test[['floor']])
    mse_rf.append(np.sqrt(np.sum((y - yhat_rf)**2)) / len(y))
    
    # Random Forest Training with Uranium as fixed effect covariate
    rfplus = RandomForestRegressor(n_estimators=100)
    rfplus.fit(train[['floor', 'Uppm']], train['log_radon'])
    yhat_rfplus = rfplus.predict(test[['floor', 'Uppm']])
    mse_rfplus.append(np.sqrt(np.sum((y - yhat_rfplus)**2)) / len(y))
    
    # Mixed Effects Random Forest Training
    mrf = MERF(n_estimators=100, max_iterations=max_iter)
    X_train =  train[['floor']]
    Z_train = np.ones((len(X_train), 1))
    clusters_train = train['county']
    y_train = train['log_radon']
    mrf.fit(X_train, Z_train, clusters_train, y_train)

    # Mixed Effects Random Forest Test
    X_test =  test[['floor']]
    Z_test = np.ones((len(X_test), 1))
    clusters_test = test['county']
    yhat_mrf = mrf.predict(X_test, Z_test, clusters_test)
    mse_mrf.append(np.sqrt(np.sum((y - yhat_mrf)**2)) / len(y))
    
    # Mixed Effects Random Forest Training with uranium RE
    mrf_uranium = MERF(n_estimators=100, max_iterations=max_iter)
    X_train =  train[['floor']]
    Z_train = np.array((np.ones(len(X_train)), train['Uppm'])).T
    clusters_train = train['county']
    y_train = train['log_radon']
    mrf_uranium.fit(X_train, Z_train, clusters_train, y_train)

    # Mixed Effects Random Forest Test with uranium RE
    X_test =  test[['floor']]
    Z_test = np.array((np.ones(len(X_test)), test['Uppm'])).T
    clusters_test = test['county']
    yhat_mrf_uranium = mrf_uranium.predict(X_test, Z_test, clusters_test)
    mse_mrf_uranium.append(np.sqrt(np.sum((y - yhat_mrf_uranium)**2)) / len(y))
    
    # Mixed Effects Random Forest Training with Uranium as both FE and RE
    mrf_uranium_both = MERF(n_estimators=100, max_iterations=max_iter)
    X_train =  train[['floor', 'Uppm']]
    Z_train = np.array((np.ones(len(X_train)), train['Uppm'])).T
    clusters_train = train['county']
    y_train = train['log_radon']
    mrf_uranium_both.fit(X_train, Z_train, clusters_train, y_train)

    # Mixed Effects Random Forest Test with County-level covariate (uranium)
    X_test =  test[['floor', 'Uppm']]
    Z_test = np.array((np.ones(len(X_test)), test['Uppm'])).T
    clusters_test = test['county']
    yhat_mrf_uranium_both = mrf_uranium_both.predict(X_test, Z_test, clusters_test)
    mse_mrf_uranium_both.append(np.sqrt(np.sum((y - yhat_mrf_uranium_both)**2)) / len(y))









    



INFO     [merf.py:235] GLL is 134.74391444720203 at iteration 1.
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In [41]:

    
mse_df = pd.DataFrame([mse_rf, mse_rfplus, mse_mrf, mse_mrf_uranium, mse_mrf_uranium_both], index=['RF', 'RF+Uppm', 'MERF', 'MERF+UppmRE', 'MERF+UppmFE+RE'])
mse_df









    Out[41]:







  
    
      
      0
      1
      2
      3
      4
    
  
  
    
      RF
      0.060080
      0.050677
      0.058140
      0.065769
      0.055718
    
    
      RF+Uppm
      0.060350
      0.052192
      0.057646
      0.063895
      0.058822
    
    
      MERF
      0.056341
      0.046672
      0.055815
      0.062039
      0.052956
    
    
      MERF+UppmRE
      0.056225
      0.045782
      0.056042
      0.062444
      0.052562
    
    
      MERF+UppmFE+RE
      0.060225
      0.051556
      0.057981
      0.064414
      0.059705



In [73]:

    
gains = (100 * (mse_df.iloc[0] - mse_df)/ mse_df.iloc[0]).mean(axis=1)
gains









    Out[73]:





RF                0.000000
RF+Uppm          -1.062278
MERF              5.750576
MERF+UppmRE       6.080535
MERF+UppmFE+RE   -1.359878
dtype: float64



In [81]:

    
gains.plot(kind='bar', style='b')
plt.grid('on')
plt.ylabel('Gain over RF')









    Out[81]:





Text(0,0.5,'Gain over RF')



In [42]:

    
mse_df.to_csv('mse_experiments.csv')



In [82]:

    
plot_training_stats(mrf_uranium)



In [83]:

    
plot_training_stats(mrf_all)



In [84]:

    
bb = plot_bhat(mrf_uranium, 23)









    



/Users/souravdey/.virtualenvs/merf/lib/python3.6/site-packages/ipykernel_launcher.py:1: DeprecationWarning: 
Panel is deprecated and will be removed in a future version.
The recommended way to represent these types of 3-dimensional data are with a MultiIndex on a DataFrame, via the Panel.to_frame() method
Alternatively, you can use the xarray package http://xarray.pydata.org/en/stable/.
Pandas provides a `.to_xarray()` method to help automate this conversion.

  """Entry point for launching an IPython kernel.



In [85]:

    
mrf_uranium.b_hat_history[-1][1].hist()









    Out[85]:





<matplotlib.axes._subplots.AxesSubplot at 0x10e4c05c0>



In [ ]:

	Unnamed: 0	Reaction	Days	Subject
0	1	249.5600	0	308
1	2	258.7047	1	308
2	3	250.8006	2	308
3	4	321.4398	3	308
4	5	356.8519	4	308

	Unnamed: 0	idnum	state	state2	stfips	zip	region	typebldg	floor	room	...	pcterr	adjwt	dupflag	cntyfips	county	fips	Uppm	county_code	log_radon
0	0	5081.0	MN	MN	27.0	55735	5.0	1.0	1.0	3.0	...	9.7	1146.499190	1.0	1.0	AITKIN	27001.0	0.502054	0	0.832909
1	1	5082.0	MN	MN	27.0	55748	5.0	1.0	0.0	4.0	...	14.5	471.366223	0.0	1.0	AITKIN	27001.0	0.502054	0	0.832909
2	2	5083.0	MN	MN	27.0	55748	5.0	1.0	0.0	4.0	...	9.6	433.316718	0.0	1.0	AITKIN	27001.0	0.502054	0	1.098612
3	3	5084.0	MN	MN	27.0	56469	5.0	1.0	0.0	4.0	...	24.3	461.623670	0.0	1.0	AITKIN	27001.0	0.502054	0	0.095310
4	4	5085.0	MN	MN	27.0	55011	3.0	1.0	0.0	4.0	...	13.8	433.316718	0.0	3.0	ANOKA	27003.0	0.428565	1	1.163151

	0	1	2	3	4
RF	0.060080	0.050677	0.058140	0.065769	0.055718
RF+Uppm	0.060350	0.052192	0.057646	0.063895	0.058822
MERF	0.056341	0.046672	0.055815	0.062039	0.052956
MERF+UppmRE	0.056225	0.045782	0.056042	0.062444	0.052562
MERF+UppmFE+RE	0.060225	0.051556	0.057981	0.064414	0.059705