Constraint Satisfaction Matcher
The ConstraintSatisfactionMatcher can be used to optimize any linear function of the baseline covariates. We support constraints on the size of the subset populations and the allowed mismatch.
Here, we demonstrate the optimization of balance subject to size constraints only. Namely, we solve:
\begin{equation} \begin{aligned} & \underset{\hat{P}}{\text{minimize}} & & \sum_k |\mu_{\hat{P}k} - \mu_{Tk}| \\ & \text{subject to} & & |\hat{P}| = P^* \\ & & & |\hat{T}| = T^* \\ \end{aligned} \end{equation}
where \(P\) and \(T\) refer to two populations we are trying to match, \(\hat{P}\) and \(\hat{T}\) are the subsets of \(P\) and \(T\) we are seeking, \(P^*\) and \(T^*\) are fixed integers, and \(k\) indexes the covariates of \(P\) and \(T\).
[1]:
import logging
logging.basicConfig(
format="%(levelname)-4s [%(filename)s:%(lineno)d] %(message)s",
level='INFO',
)
from pybalance.utils import (
BetaBalance,
BetaXBalance,
GammaBalance,
GammaXBalance,
GammaXTreeBalance
)
from pybalance.sim import generate_toy_dataset
from pybalance.lp import ConstraintSatisfactionMatcher
from pybalance.visualization import (
plot_numeric_features,
plot_categoric_features,
plot_binary_features,
plot_per_feature_loss,
)
[2]:
time_limit = 360
[3]:
m = generate_toy_dataset()
m
[3]:
Headers Numeric:
['age', 'height', 'weight']
Headers Categoric:
['gender', 'haircolor', 'country', 'binary_0', 'binary_1', 'binary_2', 'binary_3']
Populations
['pool', 'target']
['age', 'height', 'weight']
Headers Categoric:
['gender', 'haircolor', 'country', 'binary_0', 'binary_1', 'binary_2', 'binary_3']
Populations
['pool', 'target']
| age | height | weight | gender | haircolor | country | population | binary_0 | binary_1 | binary_2 | binary_3 | patient_id | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 62.511573 | 190.229250 | 105.165097 | 0.0 | 2 | 3 | pool | 0 | 0 | 0 | 0 | 0 |
| 1 | 68.505065 | 161.121236 | 95.001474 | 0.0 | 1 | 1 | pool | 1 | 0 | 1 | 0 | 1 |
| 2 | 50.071384 | 162.325356 | 84.290576 | 1.0 | 0 | 5 | pool | 0 | 0 | 1 | 1 | 2 |
| 3 | 44.423692 | 150.948096 | 82.031381 | 1.0 | 2 | 2 | pool | 0 | 0 | 0 | 1 | 3 |
| 4 | 41.695052 | 132.952651 | 54.857540 | 0.0 | 1 | 3 | pool | 0 | 0 | 1 | 1 | 4 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 995 | 21.474205 | 168.602546 | 70.342128 | 0.0 | 2 | 5 | target | 0 | 0 | 0 | 1 | 10995 |
| 996 | 40.643320 | 188.188724 | 61.611744 | 0.0 | 2 | 4 | target | 1 | 0 | 0 | 1 | 10996 |
| 997 | 29.472765 | 161.408162 | 57.214095 | 0.0 | 0 | 1 | target | 0 | 1 | 1 | 1 | 10997 |
| 998 | 41.291949 | 150.968833 | 91.270798 | 0.0 | 0 | 3 | target | 0 | 0 | 0 | 0 | 10998 |
| 999 | 67.530294 | 155.124741 | 56.196505 | 1.0 | 0 | 1 | target | 1 | 0 | 0 | 0 | 10999 |
11000 rows × 12 columns
Optimize Beta (Mean Absolute SMD)
[4]:
objective = beta = BetaBalance(m)
matcher = matcher_beta = ConstraintSatisfactionMatcher(
m,
time_limit=time_limit,
objective=objective,
ps_hinting=False,
num_workers=4)
matcher.get_params()
INFO [matcher.py:65] Scaling features by factor 240.00 in order to use integer solver with <= 0.2841% loss.
[4]:
{'objective': 'beta',
'pool_size': 1000,
'target_size': 1000,
'max_mismatch': None,
'time_limit': 360,
'num_workers': 4,
'ps_hinting': False,
'verbose': True}
[5]:
matcher.match()
INFO [matcher.py:418] Solving for match population with pool size = 1000 and target size = 1000 subject to None balance constraint.
INFO [matcher.py:421] Matching on 15 dimensions ...
INFO [matcher.py:428] Building model variables and constraints ...
INFO [matcher.py:437] Calculating bounds on feature variables ...
INFO [matcher.py:527] Applying size constraints on pool and target ...
INFO [matcher.py:611] Solving with 4 workers ...
INFO [matcher.py:90] Initial balance score: 0.2328
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 1, time = 0.02 m
INFO [matcher.py:101] Objective: 452948000.0
INFO [matcher.py:120] Balance (beta): 0.2298
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 2, time = 0.03 m
INFO [matcher.py:101] Objective: 452876000.0
INFO [matcher.py:120] Balance (beta): 0.2297
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 3, time = 0.04 m
INFO [matcher.py:101] Objective: 452777000.0
INFO [matcher.py:120] Balance (beta): 0.2297
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 4, time = 0.04 m
INFO [matcher.py:101] Objective: 452736000.0
INFO [matcher.py:120] Balance (beta): 0.2296
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 5, time = 0.04 m
INFO [matcher.py:101] Objective: 452730000.0
INFO [matcher.py:120] Balance (beta): 0.2296
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 6, time = 0.05 m
INFO [matcher.py:101] Objective: 452596000.0
INFO [matcher.py:120] Balance (beta): 0.2295
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 7, time = 0.05 m
INFO [matcher.py:101] Objective: 452537000.0
INFO [matcher.py:120] Balance (beta): 0.2294
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 8, time = 0.06 m
INFO [matcher.py:101] Objective: 452040000.0
INFO [matcher.py:120] Balance (beta): 0.2291
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 9, time = 0.08 m
INFO [matcher.py:101] Objective: 451825000.0
INFO [matcher.py:120] Balance (beta): 0.2289
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 10, time = 0.08 m
INFO [matcher.py:101] Objective: 451808000.0
INFO [matcher.py:120] Balance (beta): 0.2289
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 11, time = 0.08 m
INFO [matcher.py:101] Objective: 451755000.0
INFO [matcher.py:120] Balance (beta): 0.2289
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 12, time = 0.08 m
INFO [matcher.py:101] Objective: 22699000.0
INFO [matcher.py:120] Balance (beta): 0.0104
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 13, time = 0.10 m
INFO [matcher.py:101] Objective: 22422000.0
INFO [matcher.py:120] Balance (beta): 0.0124
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 14, time = 0.12 m
INFO [matcher.py:101] Objective: 22306000.0
INFO [matcher.py:120] Balance (beta): 0.0124
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 15, time = 0.13 m
INFO [matcher.py:101] Objective: 22291000.0
INFO [matcher.py:120] Balance (beta): 0.0124
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 16, time = 0.14 m
INFO [matcher.py:101] Objective: 22121000.0
INFO [matcher.py:120] Balance (beta): 0.0122
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 17, time = 0.16 m
INFO [matcher.py:101] Objective: 22119000.0
INFO [matcher.py:120] Balance (beta): 0.0102
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 18, time = 0.21 m
INFO [matcher.py:101] Objective: 22103000.0
INFO [matcher.py:120] Balance (beta): 0.0121
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 19, time = 0.23 m
INFO [matcher.py:101] Objective: 22101000.0
INFO [matcher.py:120] Balance (beta): 0.0122
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 20, time = 0.25 m
INFO [matcher.py:101] Objective: 22099000.0
INFO [matcher.py:120] Balance (beta): 0.0122
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 21, time = 0.27 m
INFO [matcher.py:101] Objective: 22088000.0
INFO [matcher.py:120] Balance (beta): 0.0122
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 22, time = 0.56 m
INFO [matcher.py:101] Objective: 22084000.0
INFO [matcher.py:120] Balance (beta): 0.0122
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 23, time = 3.14 m
INFO [matcher.py:101] Objective: 22083000.0
INFO [matcher.py:120] Balance (beta): 0.0101
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 24, time = 5.71 m
INFO [matcher.py:101] Objective: 22082000.0
INFO [matcher.py:120] Balance (beta): 0.0102
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:618] Status = FEASIBLE
INFO [matcher.py:619] Number of solutions found: 24
[5]:
Headers Numeric:
['age', 'height', 'weight']
Headers Categoric:
['gender', 'haircolor', 'country', 'binary_0', 'binary_1', 'binary_2', 'binary_3']
Populations
['pool', 'target']
['age', 'height', 'weight']
Headers Categoric:
['gender', 'haircolor', 'country', 'binary_0', 'binary_1', 'binary_2', 'binary_3']
Populations
['pool', 'target']
| age | height | weight | gender | haircolor | country | population | binary_0 | binary_1 | binary_2 | binary_3 | patient_id | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 55.261578 | 139.396134 | 94.438359 | 0.0 | 2 | 2 | target | 0 | 0 | 1 | 1 | 10000 |
| 1 | 63.113091 | 165.563337 | 67.433016 | 1.0 | 2 | 2 | target | 0 | 1 | 1 | 0 | 10001 |
| 2 | 58.232216 | 160.859857 | 71.915385 | 1.0 | 0 | 2 | target | 0 | 0 | 0 | 0 | 10002 |
| 3 | 58.996941 | 140.357415 | 115.606615 | 1.0 | 0 | 3 | target | 1 | 1 | 0 | 0 | 10003 |
| 4 | 36.850195 | 189.983706 | 53.000581 | 0.0 | 2 | 5 | target | 0 | 0 | 0 | 0 | 10004 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 9933 | 68.194783 | 127.495418 | 69.177329 | 0.0 | 1 | 5 | pool | 1 | 1 | 0 | 0 | 9933 |
| 9946 | 22.630370 | 185.351623 | 117.381552 | 1.0 | 1 | 2 | pool | 1 | 0 | 0 | 1 | 9946 |
| 9955 | 56.736759 | 161.612045 | 72.288182 | 1.0 | 2 | 2 | pool | 1 | 0 | 1 | 1 | 9955 |
| 9981 | 39.006118 | 133.419182 | 71.135407 | 0.0 | 1 | 4 | pool | 0 | 0 | 0 | 0 | 9981 |
| 9982 | 50.575808 | 139.401060 | 89.848616 | 0.0 | 1 | 1 | pool | 0 | 0 | 0 | 1 | 9982 |
2000 rows × 12 columns
Note that it is possible for the “balance” value to go up (hopefully only slightly) as the solver progresses. This behavior is due to rounding errors incurred when casting a continuous problem into the discrete space understood by the solver. The “balance” reported here is the actual balance computed to full accuracy on the original dataset; the “objective” value here is the actual quantity being optimized and should never increase.
[6]:
matcher.get_best_match()
[6]:
Headers Numeric:
['age', 'height', 'weight']
Headers Categoric:
['gender', 'haircolor', 'country', 'binary_0', 'binary_1', 'binary_2', 'binary_3']
Populations
['pool', 'target']
['age', 'height', 'weight']
Headers Categoric:
['gender', 'haircolor', 'country', 'binary_0', 'binary_1', 'binary_2', 'binary_3']
Populations
['pool', 'target']
| age | height | weight | gender | haircolor | country | population | binary_0 | binary_1 | binary_2 | binary_3 | patient_id | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 55.261578 | 139.396134 | 94.438359 | 0.0 | 2 | 2 | target | 0 | 0 | 1 | 1 | 10000 |
| 1 | 63.113091 | 165.563337 | 67.433016 | 1.0 | 2 | 2 | target | 0 | 1 | 1 | 0 | 10001 |
| 2 | 58.232216 | 160.859857 | 71.915385 | 1.0 | 0 | 2 | target | 0 | 0 | 0 | 0 | 10002 |
| 3 | 58.996941 | 140.357415 | 115.606615 | 1.0 | 0 | 3 | target | 1 | 1 | 0 | 0 | 10003 |
| 4 | 36.850195 | 189.983706 | 53.000581 | 0.0 | 2 | 5 | target | 0 | 0 | 0 | 0 | 10004 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 9933 | 68.194783 | 127.495418 | 69.177329 | 0.0 | 1 | 5 | pool | 1 | 1 | 0 | 0 | 9933 |
| 9946 | 22.630370 | 185.351623 | 117.381552 | 1.0 | 1 | 2 | pool | 1 | 0 | 0 | 1 | 9946 |
| 9955 | 56.736759 | 161.612045 | 72.288182 | 1.0 | 2 | 2 | pool | 1 | 0 | 1 | 1 | 9955 |
| 9981 | 39.006118 | 133.419182 | 71.135407 | 0.0 | 1 | 4 | pool | 0 | 0 | 0 | 0 | 9981 |
| 9982 | 50.575808 | 139.401060 | 89.848616 | 0.0 | 1 | 1 | pool | 0 | 0 | 0 | 1 | 9982 |
2000 rows × 12 columns
[7]:
%matplotlib inline
match = matcher.get_best_match()
m_data = m.copy().get_population('pool')
m_data.loc[:, 'population'] = m_data['population'] + ' (prematch)'
match.append(m_data)
# fig = plot_per_feature_loss(match, beta, 'target', debin=False)
fig = plot_numeric_features(match, hue_order=['pool (prematch)', 'pool', 'target', ])
fig = plot_categoric_features(match, hue_order=['pool (prematch)', 'pool', 'target'])
Optimize Beta With Cross Terms Added
Sometimes it helps to add a known (non-optimal) solution as a hint to the solver. A natural choice for hinting the solver is to take a solution from PS matching. We can choose to use the PS as a hint to the solver by passing ps_hinting=True.
[8]:
objective = beta_x = BetaXBalance(m)
matcher = matcher_betax = ConstraintSatisfactionMatcher(
m,
time_limit=time_limit,
objective=objective,
ps_hinting=True,
num_workers=4)
matcher.get_params()
INFO [preprocess.py:442] Added cross term height * weight to matching features.
INFO [preprocess.py:442] Added cross term gender * age to matching features.
INFO [preprocess.py:442] Added cross term binary_0 * binary_3 to matching features.
INFO [preprocess.py:442] Added cross term binary_0 * age to matching features.
INFO [preprocess.py:442] Added cross term binary_3 * weight to matching features.
INFO [preprocess.py:442] Added cross term binary_2 * weight to matching features.
INFO [preprocess.py:442] Added cross term binary_1 * binary_3 to matching features.
INFO [preprocess.py:442] Added cross term binary_2 * binary_3 to matching features.
INFO [preprocess.py:442] Added cross term binary_2 * height to matching features.
INFO [preprocess.py:442] Added cross term binary_0 * binary_2 to matching features.
INFO [matcher.py:65] Scaling features by factor 240.00 in order to use integer solver with <= 0.3751% loss.
[8]:
{'objective': 'beta_x',
'pool_size': 1000,
'target_size': 1000,
'max_mismatch': None,
'time_limit': 360,
'num_workers': 4,
'ps_hinting': True,
'verbose': True}
[9]:
matcher.match()
INFO [matcher.py:418] Solving for match population with pool size = 1000 and target size = 1000 subject to None balance constraint.
INFO [matcher.py:421] Matching on 25 dimensions ...
INFO [matcher.py:428] Building model variables and constraints ...
INFO [matcher.py:437] Calculating bounds on feature variables ...
INFO [matcher.py:527] Applying size constraints on pool and target ...
INFO [matcher.py:533] Applying hint ...
INFO [matcher.py:540] Training PS model as guide for solver ...
/opt/miniconda3/envs/pybalance/lib/python3.9/site-packages/pybalance/lp/matcher.py:542: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
target.loc[:, "ix"] = list(range(len(target)))
/opt/miniconda3/envs/pybalance/lib/python3.9/site-packages/pybalance/lp/matcher.py:543: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
pool.loc[:, "ix"] = list(range(len(pool)))
INFO [preprocess.py:442] Added cross term height * weight to matching features.
INFO [preprocess.py:442] Added cross term gender * age to matching features.
INFO [preprocess.py:442] Added cross term binary_0 * binary_3 to matching features.
INFO [preprocess.py:442] Added cross term binary_0 * age to matching features.
INFO [preprocess.py:442] Added cross term binary_3 * weight to matching features.
INFO [preprocess.py:442] Added cross term binary_2 * weight to matching features.
INFO [preprocess.py:442] Added cross term binary_1 * binary_3 to matching features.
INFO [preprocess.py:442] Added cross term binary_2 * binary_3 to matching features.
INFO [preprocess.py:442] Added cross term binary_2 * height to matching features.
INFO [preprocess.py:442] Added cross term binary_0 * binary_2 to matching features.
INFO [matcher.py:180] Training model SGDClassifier (iter 1/50, 0.001 min) ...
INFO [matcher.py:136] Best propensity score match found:
INFO [matcher.py:137] Model: SGDClassifier
INFO [matcher.py:139] * alpha: 0.1045355473186929
INFO [matcher.py:139] * class_weight: None
INFO [matcher.py:139] * early_stopping: False
INFO [matcher.py:139] * fit_intercept: False
INFO [matcher.py:139] * loss: modified_huber
INFO [matcher.py:139] * max_iter: 1500
INFO [matcher.py:139] * penalty: l1
INFO [matcher.py:140] Score (beta_x): 0.1650
INFO [matcher.py:141] Solution time: 0.002 min
INFO [matcher.py:180] Training model SGDClassifier (iter 2/50, 0.002 min) ...
INFO [matcher.py:136] Best propensity score match found:
INFO [matcher.py:137] Model: SGDClassifier
INFO [matcher.py:139] * alpha: 0.0354658577371722
INFO [matcher.py:139] * class_weight: None
INFO [matcher.py:139] * early_stopping: False
INFO [matcher.py:139] * fit_intercept: False
INFO [matcher.py:139] * loss: modified_huber
INFO [matcher.py:139] * max_iter: 1500
INFO [matcher.py:139] * penalty: l2
INFO [matcher.py:140] Score (beta_x): 0.0887
INFO [matcher.py:141] Solution time: 0.003 min
INFO [matcher.py:180] Training model LogisticRegression (iter 3/50, 0.003 min) ...
INFO [matcher.py:136] Best propensity score match found:
INFO [matcher.py:137] Model: LogisticRegression
INFO [matcher.py:139] * C: 0.7627616953429366
INFO [matcher.py:139] * fit_intercept: True
INFO [matcher.py:139] * max_iter: 500
INFO [matcher.py:139] * penalty: l2
INFO [matcher.py:139] * solver: saga
INFO [matcher.py:140] Score (beta_x): 0.0670
INFO [matcher.py:141] Solution time: 0.006 min
INFO [matcher.py:180] Training model LogisticRegression (iter 4/50, 0.006 min) ...
INFO [matcher.py:136] Best propensity score match found:
INFO [matcher.py:137] Model: LogisticRegression
INFO [matcher.py:139] * C: 0.015804928600429955
INFO [matcher.py:139] * fit_intercept: True
INFO [matcher.py:139] * max_iter: 500
INFO [matcher.py:139] * penalty: l2
INFO [matcher.py:139] * solver: saga
INFO [matcher.py:140] Score (beta_x): 0.0615
INFO [matcher.py:141] Solution time: 0.007 min
INFO [matcher.py:180] Training model LogisticRegression (iter 5/50, 0.007 min) ...
/opt/miniconda3/envs/pybalance/lib/python3.9/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
warnings.warn(
INFO [matcher.py:180] Training model LogisticRegression (iter 6/50, 0.024 min) ...
/opt/miniconda3/envs/pybalance/lib/python3.9/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
warnings.warn(
INFO [matcher.py:180] Training model LogisticRegression (iter 7/50, 0.046 min) ...
/opt/miniconda3/envs/pybalance/lib/python3.9/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
warnings.warn(
INFO [matcher.py:180] Training model LogisticRegression (iter 8/50, 0.067 min) ...
INFO [matcher.py:180] Training model LogisticRegression (iter 9/50, 0.068 min) ...
/opt/miniconda3/envs/pybalance/lib/python3.9/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
warnings.warn(
INFO [matcher.py:180] Training model LogisticRegression (iter 10/50, 0.085 min) ...
INFO [matcher.py:180] Training model SGDClassifier (iter 11/50, 0.104 min) ...
INFO [matcher.py:180] Training model LogisticRegression (iter 12/50, 0.105 min) ...
INFO [matcher.py:180] Training model LogisticRegression (iter 13/50, 0.118 min) ...
/opt/miniconda3/envs/pybalance/lib/python3.9/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
warnings.warn(
INFO [matcher.py:180] Training model SGDClassifier (iter 14/50, 0.139 min) ...
INFO [matcher.py:180] Training model LogisticRegression (iter 15/50, 0.140 min) ...
INFO [matcher.py:180] Training model LogisticRegression (iter 16/50, 0.141 min) ...
/opt/miniconda3/envs/pybalance/lib/python3.9/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
warnings.warn(
INFO [matcher.py:180] Training model SGDClassifier (iter 17/50, 0.163 min) ...
INFO [matcher.py:180] Training model SGDClassifier (iter 18/50, 0.164 min) ...
INFO [matcher.py:180] Training model SGDClassifier (iter 19/50, 0.165 min) ...
INFO [matcher.py:180] Training model SGDClassifier (iter 20/50, 0.166 min) ...
INFO [matcher.py:180] Training model LogisticRegression (iter 21/50, 0.167 min) ...
/opt/miniconda3/envs/pybalance/lib/python3.9/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
warnings.warn(
INFO [matcher.py:180] Training model SGDClassifier (iter 22/50, 0.188 min) ...
INFO [matcher.py:180] Training model SGDClassifier (iter 23/50, 0.189 min) ...
INFO [matcher.py:180] Training model SGDClassifier (iter 24/50, 0.190 min) ...
INFO [matcher.py:180] Training model SGDClassifier (iter 25/50, 0.191 min) ...
INFO [matcher.py:180] Training model SGDClassifier (iter 26/50, 0.192 min) ...
INFO [matcher.py:180] Training model SGDClassifier (iter 27/50, 0.196 min) ...
INFO [matcher.py:180] Training model LogisticRegression (iter 28/50, 0.198 min) ...
/opt/miniconda3/envs/pybalance/lib/python3.9/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
warnings.warn(
INFO [matcher.py:180] Training model SGDClassifier (iter 29/50, 0.214 min) ...
INFO [matcher.py:180] Training model LogisticRegression (iter 30/50, 0.215 min) ...
INFO [matcher.py:180] Training model LogisticRegression (iter 31/50, 0.230 min) ...
/opt/miniconda3/envs/pybalance/lib/python3.9/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
warnings.warn(
INFO [matcher.py:180] Training model SGDClassifier (iter 32/50, 0.251 min) ...
INFO [matcher.py:180] Training model LogisticRegression (iter 33/50, 0.252 min) ...
INFO [matcher.py:180] Training model SGDClassifier (iter 34/50, 0.254 min) ...
INFO [matcher.py:136] Best propensity score match found:
INFO [matcher.py:137] Model: SGDClassifier
INFO [matcher.py:139] * alpha: 0.02606111348517078
INFO [matcher.py:139] * class_weight: balanced
INFO [matcher.py:139] * early_stopping: True
INFO [matcher.py:139] * fit_intercept: False
INFO [matcher.py:139] * loss: log_loss
INFO [matcher.py:139] * max_iter: 1500
INFO [matcher.py:139] * penalty: l2
INFO [matcher.py:140] Score (beta_x): 0.0603
INFO [matcher.py:141] Solution time: 0.255 min
INFO [matcher.py:180] Training model SGDClassifier (iter 35/50, 0.255 min) ...
INFO [matcher.py:180] Training model LogisticRegression (iter 36/50, 0.256 min) ...
/opt/miniconda3/envs/pybalance/lib/python3.9/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
warnings.warn(
INFO [matcher.py:180] Training model LogisticRegression (iter 37/50, 0.278 min) ...
INFO [matcher.py:180] Training model SGDClassifier (iter 38/50, 0.280 min) ...
INFO [matcher.py:180] Training model LogisticRegression (iter 39/50, 0.281 min) ...
/opt/miniconda3/envs/pybalance/lib/python3.9/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
warnings.warn(
INFO [matcher.py:180] Training model LogisticRegression (iter 40/50, 0.302 min) ...
INFO [matcher.py:180] Training model LogisticRegression (iter 41/50, 0.319 min) ...
INFO [matcher.py:180] Training model LogisticRegression (iter 42/50, 0.328 min) ...
INFO [matcher.py:180] Training model SGDClassifier (iter 43/50, 0.330 min) ...
INFO [matcher.py:180] Training model LogisticRegression (iter 44/50, 0.331 min) ...
INFO [matcher.py:180] Training model LogisticRegression (iter 45/50, 0.344 min) ...
INFO [matcher.py:180] Training model SGDClassifier (iter 46/50, 0.351 min) ...
INFO [matcher.py:180] Training model SGDClassifier (iter 47/50, 0.352 min) ...
INFO [matcher.py:180] Training model SGDClassifier (iter 48/50, 0.354 min) ...
INFO [matcher.py:180] Training model SGDClassifier (iter 49/50, 0.355 min) ...
INFO [matcher.py:180] Training model SGDClassifier (iter 50/50, 0.356 min) ...
INFO [matcher.py:136] Best propensity score match found:
INFO [matcher.py:137] Model: SGDClassifier
INFO [matcher.py:139] * alpha: 0.02606111348517078
INFO [matcher.py:139] * class_weight: balanced
INFO [matcher.py:139] * early_stopping: True
INFO [matcher.py:139] * fit_intercept: False
INFO [matcher.py:139] * loss: log_loss
INFO [matcher.py:139] * max_iter: 1500
INFO [matcher.py:139] * penalty: l2
INFO [matcher.py:140] Score (beta_x): 0.0603
INFO [matcher.py:141] Solution time: 0.255 min
INFO [matcher.py:577] Hint achieves objective value = 147332.
INFO [matcher.py:579] Applying hints ...
INFO [matcher.py:611] Solving with 4 workers ...
INFO [matcher.py:90] Initial balance score: 0.2324
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 1, time = 0.08 m
INFO [matcher.py:101] Objective: 147332000.0
INFO [matcher.py:120] Balance (beta_x): 0.0603
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 2, time = 0.08 m
INFO [matcher.py:101] Objective: 147176000.0
INFO [matcher.py:120] Balance (beta_x): 0.0601
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 3, time = 0.09 m
INFO [matcher.py:101] Objective: 147050000.0
INFO [matcher.py:120] Balance (beta_x): 0.0601
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 4, time = 0.09 m
INFO [matcher.py:101] Objective: 147002000.0
INFO [matcher.py:120] Balance (beta_x): 0.0601
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 5, time = 0.09 m
INFO [matcher.py:101] Objective: 146721000.0
INFO [matcher.py:120] Balance (beta_x): 0.0601
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 6, time = 0.10 m
INFO [matcher.py:101] Objective: 145561000.0
INFO [matcher.py:120] Balance (beta_x): 0.0596
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 7, time = 0.10 m
INFO [matcher.py:101] Objective: 145369000.0
INFO [matcher.py:120] Balance (beta_x): 0.0596
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 8, time = 0.13 m
INFO [matcher.py:101] Objective: 145229000.0
INFO [matcher.py:120] Balance (beta_x): 0.0596
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 9, time = 0.13 m
INFO [matcher.py:101] Objective: 145192000.0
INFO [matcher.py:120] Balance (beta_x): 0.0595
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 10, time = 0.13 m
INFO [matcher.py:101] Objective: 145144000.0
INFO [matcher.py:120] Balance (beta_x): 0.0596
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 11, time = 0.14 m
INFO [matcher.py:101] Objective: 145129000.0
INFO [matcher.py:120] Balance (beta_x): 0.0595
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 12, time = 0.14 m
INFO [matcher.py:101] Objective: 144996000.0
INFO [matcher.py:120] Balance (beta_x): 0.0595
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 13, time = 0.16 m
INFO [matcher.py:101] Objective: 144843000.0
INFO [matcher.py:120] Balance (beta_x): 0.0594
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 14, time = 0.16 m
INFO [matcher.py:101] Objective: 144588000.0
INFO [matcher.py:120] Balance (beta_x): 0.0594
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 15, time = 0.20 m
INFO [matcher.py:101] Objective: 144406000.0
INFO [matcher.py:120] Balance (beta_x): 0.0594
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 16, time = 0.21 m
INFO [matcher.py:101] Objective: 144195000.0
INFO [matcher.py:120] Balance (beta_x): 0.0593
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 17, time = 0.23 m
INFO [matcher.py:101] Objective: 143827000.0
INFO [matcher.py:120] Balance (beta_x): 0.0593
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 18, time = 0.23 m
INFO [matcher.py:101] Objective: 143679000.0
INFO [matcher.py:120] Balance (beta_x): 0.0592
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 19, time = 0.25 m
INFO [matcher.py:101] Objective: 143649000.0
INFO [matcher.py:120] Balance (beta_x): 0.0591
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 20, time = 0.26 m
INFO [matcher.py:101] Objective: 143574000.0
INFO [matcher.py:120] Balance (beta_x): 0.0591
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 21, time = 0.27 m
INFO [matcher.py:101] Objective: 143526000.0
INFO [matcher.py:120] Balance (beta_x): 0.0592
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 22, time = 0.27 m
INFO [matcher.py:101] Objective: 143245000.0
INFO [matcher.py:120] Balance (beta_x): 0.0591
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 23, time = 0.29 m
INFO [matcher.py:101] Objective: 142588000.0
INFO [matcher.py:120] Balance (beta_x): 0.0589
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 24, time = 0.31 m
INFO [matcher.py:101] Objective: 142455000.0
INFO [matcher.py:120] Balance (beta_x): 0.0589
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 25, time = 0.34 m
INFO [matcher.py:101] Objective: 141967000.0
INFO [matcher.py:120] Balance (beta_x): 0.0588
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 26, time = 0.35 m
INFO [matcher.py:101] Objective: 141924000.0
INFO [matcher.py:120] Balance (beta_x): 0.0588
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 27, time = 0.35 m
INFO [matcher.py:101] Objective: 141829000.0
INFO [matcher.py:120] Balance (beta_x): 0.0587
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 28, time = 0.35 m
INFO [matcher.py:101] Objective: 141681000.0
INFO [matcher.py:120] Balance (beta_x): 0.0587
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 29, time = 0.38 m
INFO [matcher.py:101] Objective: 141606000.0
INFO [matcher.py:120] Balance (beta_x): 0.0587
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 30, time = 0.38 m
INFO [matcher.py:101] Objective: 141322000.0
INFO [matcher.py:120] Balance (beta_x): 0.0585
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 31, time = 0.39 m
INFO [matcher.py:101] Objective: 141285000.0
INFO [matcher.py:120] Balance (beta_x): 0.0585
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 32, time = 0.39 m
INFO [matcher.py:101] Objective: 141229000.0
INFO [matcher.py:120] Balance (beta_x): 0.0586
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 33, time = 0.43 m
INFO [matcher.py:101] Objective: 141161000.0
INFO [matcher.py:120] Balance (beta_x): 0.0586
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 34, time = 0.43 m
INFO [matcher.py:101] Objective: 140938000.0
INFO [matcher.py:120] Balance (beta_x): 0.0584
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 35, time = 0.44 m
INFO [matcher.py:101] Objective: 140793000.0
INFO [matcher.py:120] Balance (beta_x): 0.0584
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 36, time = 0.44 m
INFO [matcher.py:101] Objective: 140697000.0
INFO [matcher.py:120] Balance (beta_x): 0.0584
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 37, time = 0.45 m
INFO [matcher.py:101] Objective: 140642000.0
INFO [matcher.py:120] Balance (beta_x): 0.0584
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 38, time = 0.45 m
INFO [matcher.py:101] Objective: 140592000.0
INFO [matcher.py:120] Balance (beta_x): 0.0584
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 39, time = 0.47 m
INFO [matcher.py:101] Objective: 140562000.0
INFO [matcher.py:120] Balance (beta_x): 0.0583
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 40, time = 0.47 m
INFO [matcher.py:101] Objective: 140520000.0
INFO [matcher.py:120] Balance (beta_x): 0.0583
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 41, time = 0.62 m
INFO [matcher.py:101] Objective: 140167000.0
INFO [matcher.py:120] Balance (beta_x): 0.0582
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 42, time = 0.65 m
INFO [matcher.py:101] Objective: 140140000.0
INFO [matcher.py:120] Balance (beta_x): 0.0583
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 43, time = 0.66 m
INFO [matcher.py:101] Objective: 140132000.0
INFO [matcher.py:120] Balance (beta_x): 0.0583
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 44, time = 0.67 m
INFO [matcher.py:101] Objective: 139980000.0
INFO [matcher.py:120] Balance (beta_x): 0.0583
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 45, time = 0.68 m
INFO [matcher.py:101] Objective: 139917000.0
INFO [matcher.py:120] Balance (beta_x): 0.0583
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 46, time = 0.68 m
INFO [matcher.py:101] Objective: 139651000.0
INFO [matcher.py:120] Balance (beta_x): 0.0582
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 47, time = 0.70 m
INFO [matcher.py:101] Objective: 139638000.0
INFO [matcher.py:120] Balance (beta_x): 0.0583
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 48, time = 0.72 m
INFO [matcher.py:101] Objective: 139610000.0
INFO [matcher.py:120] Balance (beta_x): 0.0583
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 49, time = 0.74 m
INFO [matcher.py:101] Objective: 139189000.0
INFO [matcher.py:120] Balance (beta_x): 0.0581
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 50, time = 0.76 m
INFO [matcher.py:101] Objective: 139131000.0
INFO [matcher.py:120] Balance (beta_x): 0.0580
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 51, time = 0.76 m
INFO [matcher.py:101] Objective: 138919000.0
INFO [matcher.py:120] Balance (beta_x): 0.0580
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 52, time = 0.76 m
INFO [matcher.py:101] Objective: 138885000.0
INFO [matcher.py:120] Balance (beta_x): 0.0580
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 53, time = 0.78 m
INFO [matcher.py:101] Objective: 138879000.0
INFO [matcher.py:120] Balance (beta_x): 0.0580
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 54, time = 0.80 m
INFO [matcher.py:101] Objective: 138853000.0
INFO [matcher.py:120] Balance (beta_x): 0.0579
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 55, time = 0.83 m
INFO [matcher.py:101] Objective: 138782000.0
INFO [matcher.py:120] Balance (beta_x): 0.0579
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 56, time = 0.83 m
INFO [matcher.py:101] Objective: 138716000.0
INFO [matcher.py:120] Balance (beta_x): 0.0579
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 57, time = 0.85 m
INFO [matcher.py:101] Objective: 138683000.0
INFO [matcher.py:120] Balance (beta_x): 0.0579
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 58, time = 0.85 m
INFO [matcher.py:101] Objective: 138483000.0
INFO [matcher.py:120] Balance (beta_x): 0.0579
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 59, time = 0.88 m
INFO [matcher.py:101] Objective: 137815000.0
INFO [matcher.py:120] Balance (beta_x): 0.0576
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 60, time = 0.88 m
INFO [matcher.py:101] Objective: 137752000.0
INFO [matcher.py:120] Balance (beta_x): 0.0576
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 61, time = 0.88 m
INFO [matcher.py:101] Objective: 137486000.0
INFO [matcher.py:120] Balance (beta_x): 0.0575
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 62, time = 0.89 m
INFO [matcher.py:101] Objective: 137401000.0
INFO [matcher.py:120] Balance (beta_x): 0.0575
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 63, time = 0.90 m
INFO [matcher.py:101] Objective: 137333000.0
INFO [matcher.py:120] Balance (beta_x): 0.0575
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 64, time = 0.93 m
INFO [matcher.py:101] Objective: 137132000.0
INFO [matcher.py:120] Balance (beta_x): 0.0574
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 65, time = 0.93 m
INFO [matcher.py:101] Objective: 137105000.0
INFO [matcher.py:120] Balance (beta_x): 0.0574
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 66, time = 0.93 m
INFO [matcher.py:101] Objective: 137099000.0
INFO [matcher.py:120] Balance (beta_x): 0.0574
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 67, time = 0.98 m
INFO [matcher.py:101] Objective: 137080000.0
INFO [matcher.py:120] Balance (beta_x): 0.0575
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 68, time = 0.98 m
INFO [matcher.py:101] Objective: 137005000.0
INFO [matcher.py:120] Balance (beta_x): 0.0574
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 69, time = 0.99 m
INFO [matcher.py:101] Objective: 136985000.0
INFO [matcher.py:120] Balance (beta_x): 0.0574
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 70, time = 1.04 m
INFO [matcher.py:101] Objective: 136974000.0
INFO [matcher.py:120] Balance (beta_x): 0.0574
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 71, time = 1.04 m
INFO [matcher.py:101] Objective: 136947000.0
INFO [matcher.py:120] Balance (beta_x): 0.0574
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 72, time = 1.10 m
INFO [matcher.py:101] Objective: 136838000.0
INFO [matcher.py:120] Balance (beta_x): 0.0574
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 73, time = 1.21 m
INFO [matcher.py:101] Objective: 136729000.0
INFO [matcher.py:120] Balance (beta_x): 0.0573
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 74, time = 1.24 m
INFO [matcher.py:101] Objective: 136654000.0
INFO [matcher.py:120] Balance (beta_x): 0.0574
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 75, time = 1.24 m
INFO [matcher.py:101] Objective: 136559000.0
INFO [matcher.py:120] Balance (beta_x): 0.0574
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 76, time = 1.25 m
INFO [matcher.py:101] Objective: 136503000.0
INFO [matcher.py:120] Balance (beta_x): 0.0573
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 77, time = 1.25 m
INFO [matcher.py:101] Objective: 136457000.0
INFO [matcher.py:120] Balance (beta_x): 0.0573
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 78, time = 1.26 m
INFO [matcher.py:101] Objective: 136435000.0
INFO [matcher.py:120] Balance (beta_x): 0.0573
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 79, time = 1.36 m
INFO [matcher.py:101] Objective: 136386000.0
INFO [matcher.py:120] Balance (beta_x): 0.0574
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 80, time = 1.37 m
INFO [matcher.py:101] Objective: 136349000.0
INFO [matcher.py:120] Balance (beta_x): 0.0573
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 81, time = 1.38 m
INFO [matcher.py:101] Objective: 136313000.0
INFO [matcher.py:120] Balance (beta_x): 0.0573
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 82, time = 1.38 m
INFO [matcher.py:101] Objective: 136286000.0
INFO [matcher.py:120] Balance (beta_x): 0.0573
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 83, time = 1.38 m
INFO [matcher.py:101] Objective: 136020000.0
INFO [matcher.py:120] Balance (beta_x): 0.0573
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 84, time = 1.41 m
INFO [matcher.py:101] Objective: 135535000.0
INFO [matcher.py:120] Balance (beta_x): 0.0570
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 85, time = 1.47 m
INFO [matcher.py:101] Objective: 135513000.0
INFO [matcher.py:120] Balance (beta_x): 0.0570
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 86, time = 1.48 m
INFO [matcher.py:101] Objective: 135473000.0
INFO [matcher.py:120] Balance (beta_x): 0.0570
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 87, time = 1.48 m
INFO [matcher.py:101] Objective: 135452000.0
INFO [matcher.py:120] Balance (beta_x): 0.0570
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 88, time = 1.51 m
INFO [matcher.py:101] Objective: 135375000.0
INFO [matcher.py:120] Balance (beta_x): 0.0570
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 89, time = 1.53 m
INFO [matcher.py:101] Objective: 135312000.0
INFO [matcher.py:120] Balance (beta_x): 0.0569
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 90, time = 1.54 m
INFO [matcher.py:101] Objective: 134988000.0
INFO [matcher.py:120] Balance (beta_x): 0.0568
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 91, time = 1.58 m
INFO [matcher.py:101] Objective: 134977000.0
INFO [matcher.py:120] Balance (beta_x): 0.0568
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 92, time = 1.59 m
INFO [matcher.py:101] Objective: 134937000.0
INFO [matcher.py:120] Balance (beta_x): 0.0568
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 93, time = 1.59 m
INFO [matcher.py:101] Objective: 134931000.0
INFO [matcher.py:120] Balance (beta_x): 0.0568
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 94, time = 1.61 m
INFO [matcher.py:101] Objective: 134909000.0
INFO [matcher.py:120] Balance (beta_x): 0.0567
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 95, time = 1.66 m
INFO [matcher.py:101] Objective: 134887000.0
INFO [matcher.py:120] Balance (beta_x): 0.0566
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 96, time = 1.67 m
INFO [matcher.py:101] Objective: 134614000.0
INFO [matcher.py:120] Balance (beta_x): 0.0566
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 97, time = 1.68 m
INFO [matcher.py:101] Objective: 134579000.0
INFO [matcher.py:120] Balance (beta_x): 0.0566
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 98, time = 1.68 m
INFO [matcher.py:101] Objective: 134522000.0
INFO [matcher.py:120] Balance (beta_x): 0.0566
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 99, time = 1.68 m
INFO [matcher.py:101] Objective: 134400000.0
INFO [matcher.py:120] Balance (beta_x): 0.0565
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 100, time = 1.69 m
INFO [matcher.py:101] Objective: 134234000.0
INFO [matcher.py:120] Balance (beta_x): 0.0565
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 101, time = 1.69 m
INFO [matcher.py:101] Objective: 134107000.0
INFO [matcher.py:120] Balance (beta_x): 0.0564
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 102, time = 1.73 m
INFO [matcher.py:101] Objective: 133877000.0
INFO [matcher.py:120] Balance (beta_x): 0.0564
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 103, time = 1.74 m
INFO [matcher.py:101] Objective: 133872000.0
INFO [matcher.py:120] Balance (beta_x): 0.0564
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 104, time = 1.77 m
INFO [matcher.py:101] Objective: 133834000.0
INFO [matcher.py:120] Balance (beta_x): 0.0562
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 105, time = 1.79 m
INFO [matcher.py:101] Objective: 133706000.0
INFO [matcher.py:120] Balance (beta_x): 0.0561
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 106, time = 1.84 m
INFO [matcher.py:101] Objective: 133469000.0
INFO [matcher.py:120] Balance (beta_x): 0.0560
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 107, time = 1.87 m
INFO [matcher.py:101] Objective: 133214000.0
INFO [matcher.py:120] Balance (beta_x): 0.0559
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 108, time = 1.87 m
INFO [matcher.py:101] Objective: 133090000.0
INFO [matcher.py:120] Balance (beta_x): 0.0559
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 109, time = 1.87 m
INFO [matcher.py:101] Objective: 133063000.0
INFO [matcher.py:120] Balance (beta_x): 0.0559
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 110, time = 1.88 m
INFO [matcher.py:101] Objective: 133033000.0
INFO [matcher.py:120] Balance (beta_x): 0.0559
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 111, time = 1.89 m
INFO [matcher.py:101] Objective: 133028000.0
INFO [matcher.py:120] Balance (beta_x): 0.0559
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 112, time = 1.90 m
INFO [matcher.py:101] Objective: 133008000.0
INFO [matcher.py:120] Balance (beta_x): 0.0558
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 113, time = 1.91 m
INFO [matcher.py:101] Objective: 132983000.0
INFO [matcher.py:120] Balance (beta_x): 0.0559
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 114, time = 1.91 m
INFO [matcher.py:101] Objective: 132978000.0
INFO [matcher.py:120] Balance (beta_x): 0.0559
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 115, time = 1.95 m
INFO [matcher.py:101] Objective: 132841000.0
INFO [matcher.py:120] Balance (beta_x): 0.0558
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 116, time = 1.96 m
INFO [matcher.py:101] Objective: 132785000.0
INFO [matcher.py:120] Balance (beta_x): 0.0558
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 117, time = 1.96 m
INFO [matcher.py:101] Objective: 132779000.0
INFO [matcher.py:120] Balance (beta_x): 0.0558
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 118, time = 2.02 m
INFO [matcher.py:101] Objective: 132600000.0
INFO [matcher.py:120] Balance (beta_x): 0.0557
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 119, time = 2.02 m
INFO [matcher.py:101] Objective: 132480000.0
INFO [matcher.py:120] Balance (beta_x): 0.0557
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 120, time = 2.03 m
INFO [matcher.py:101] Objective: 132453000.0
INFO [matcher.py:120] Balance (beta_x): 0.0557
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 121, time = 2.04 m
INFO [matcher.py:101] Objective: 132264000.0
INFO [matcher.py:120] Balance (beta_x): 0.0556
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 122, time = 2.16 m
INFO [matcher.py:101] Objective: 132136000.0
INFO [matcher.py:120] Balance (beta_x): 0.0557
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 123, time = 2.19 m
INFO [matcher.py:101] Objective: 132122000.0
INFO [matcher.py:120] Balance (beta_x): 0.0557
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 124, time = 2.21 m
INFO [matcher.py:101] Objective: 132117000.0
INFO [matcher.py:120] Balance (beta_x): 0.0557
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 125, time = 2.21 m
INFO [matcher.py:101] Objective: 132079000.0
INFO [matcher.py:120] Balance (beta_x): 0.0557
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 126, time = 2.31 m
INFO [matcher.py:101] Objective: 132025000.0
INFO [matcher.py:120] Balance (beta_x): 0.0556
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 127, time = 2.33 m
INFO [matcher.py:101] Objective: 132007000.0
INFO [matcher.py:120] Balance (beta_x): 0.0556
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 128, time = 2.33 m
INFO [matcher.py:101] Objective: 131985000.0
INFO [matcher.py:120] Balance (beta_x): 0.0556
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 129, time = 2.33 m
INFO [matcher.py:101] Objective: 131979000.0
INFO [matcher.py:120] Balance (beta_x): 0.0556
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 130, time = 2.35 m
INFO [matcher.py:101] Objective: 131957000.0
INFO [matcher.py:120] Balance (beta_x): 0.0555
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 131, time = 2.39 m
INFO [matcher.py:101] Objective: 131715000.0
INFO [matcher.py:120] Balance (beta_x): 0.0555
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 132, time = 2.47 m
INFO [matcher.py:101] Objective: 131695000.0
INFO [matcher.py:120] Balance (beta_x): 0.0555
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 133, time = 2.49 m
INFO [matcher.py:101] Objective: 131689000.0
INFO [matcher.py:120] Balance (beta_x): 0.0554
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 134, time = 2.51 m
INFO [matcher.py:101] Objective: 131687000.0
INFO [matcher.py:120] Balance (beta_x): 0.0556
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 135, time = 2.51 m
INFO [matcher.py:101] Objective: 131585000.0
INFO [matcher.py:120] Balance (beta_x): 0.0556
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 136, time = 2.52 m
INFO [matcher.py:101] Objective: 131572000.0
INFO [matcher.py:120] Balance (beta_x): 0.0556
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 137, time = 2.52 m
INFO [matcher.py:101] Objective: 131526000.0
INFO [matcher.py:120] Balance (beta_x): 0.0555
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 138, time = 2.52 m
INFO [matcher.py:101] Objective: 131488000.0
INFO [matcher.py:120] Balance (beta_x): 0.0556
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 139, time = 2.58 m
INFO [matcher.py:101] Objective: 131421000.0
INFO [matcher.py:120] Balance (beta_x): 0.0555
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 140, time = 2.58 m
INFO [matcher.py:101] Objective: 131418000.0
INFO [matcher.py:120] Balance (beta_x): 0.0555
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 141, time = 2.60 m
INFO [matcher.py:101] Objective: 131344000.0
INFO [matcher.py:120] Balance (beta_x): 0.0555
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 142, time = 2.61 m
INFO [matcher.py:101] Objective: 131338000.0
INFO [matcher.py:120] Balance (beta_x): 0.0555
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 143, time = 2.61 m
INFO [matcher.py:101] Objective: 131174000.0
INFO [matcher.py:120] Balance (beta_x): 0.0555
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 144, time = 2.62 m
INFO [matcher.py:101] Objective: 131116000.0
INFO [matcher.py:120] Balance (beta_x): 0.0554
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 145, time = 2.73 m
INFO [matcher.py:101] Objective: 131101000.0
INFO [matcher.py:120] Balance (beta_x): 0.0554
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 146, time = 2.73 m
INFO [matcher.py:101] Objective: 131083000.0
INFO [matcher.py:120] Balance (beta_x): 0.0554
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 147, time = 2.74 m
INFO [matcher.py:101] Objective: 131069000.0
INFO [matcher.py:120] Balance (beta_x): 0.0554
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 148, time = 2.74 m
INFO [matcher.py:101] Objective: 131061000.0
INFO [matcher.py:120] Balance (beta_x): 0.0554
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 149, time = 2.74 m
INFO [matcher.py:101] Objective: 131055000.0
INFO [matcher.py:120] Balance (beta_x): 0.0554
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 150, time = 2.74 m
INFO [matcher.py:101] Objective: 131053000.0
INFO [matcher.py:120] Balance (beta_x): 0.0554
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 151, time = 2.75 m
INFO [matcher.py:101] Objective: 131017000.0
INFO [matcher.py:120] Balance (beta_x): 0.0554
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 152, time = 2.77 m
INFO [matcher.py:101] Objective: 130827000.0
INFO [matcher.py:120] Balance (beta_x): 0.0553
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 153, time = 2.77 m
INFO [matcher.py:101] Objective: 130822000.0
INFO [matcher.py:120] Balance (beta_x): 0.0553
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 154, time = 2.79 m
INFO [matcher.py:101] Objective: 130586000.0
INFO [matcher.py:120] Balance (beta_x): 0.0553
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 155, time = 2.81 m
INFO [matcher.py:101] Objective: 130560000.0
INFO [matcher.py:120] Balance (beta_x): 0.0552
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 156, time = 2.82 m
INFO [matcher.py:101] Objective: 130554000.0
INFO [matcher.py:120] Balance (beta_x): 0.0553
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 157, time = 2.83 m
INFO [matcher.py:101] Objective: 130462000.0
INFO [matcher.py:120] Balance (beta_x): 0.0553
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 158, time = 2.84 m
INFO [matcher.py:101] Objective: 130390000.0
INFO [matcher.py:120] Balance (beta_x): 0.0552
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 159, time = 2.87 m
INFO [matcher.py:101] Objective: 130376000.0
INFO [matcher.py:120] Balance (beta_x): 0.0552
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 160, time = 2.93 m
INFO [matcher.py:101] Objective: 130053000.0
INFO [matcher.py:120] Balance (beta_x): 0.0552
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 161, time = 2.93 m
INFO [matcher.py:101] Objective: 130016000.0
INFO [matcher.py:120] Balance (beta_x): 0.0551
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 162, time = 2.93 m
INFO [matcher.py:101] Objective: 129852000.0
INFO [matcher.py:120] Balance (beta_x): 0.0551
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 163, time = 2.94 m
INFO [matcher.py:101] Objective: 129796000.0
INFO [matcher.py:120] Balance (beta_x): 0.0551
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 164, time = 2.94 m
INFO [matcher.py:101] Objective: 129752000.0
INFO [matcher.py:120] Balance (beta_x): 0.0551
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 165, time = 2.94 m
INFO [matcher.py:101] Objective: 129638000.0
INFO [matcher.py:120] Balance (beta_x): 0.0550
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 166, time = 2.98 m
INFO [matcher.py:101] Objective: 129415000.0
INFO [matcher.py:120] Balance (beta_x): 0.0550
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 167, time = 3.04 m
INFO [matcher.py:101] Objective: 129413000.0
INFO [matcher.py:120] Balance (beta_x): 0.0549
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 168, time = 3.05 m
INFO [matcher.py:101] Objective: 129338000.0
INFO [matcher.py:120] Balance (beta_x): 0.0549
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 169, time = 3.07 m
INFO [matcher.py:101] Objective: 129216000.0
INFO [matcher.py:120] Balance (beta_x): 0.0548
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 170, time = 3.11 m
INFO [matcher.py:101] Objective: 129055000.0
INFO [matcher.py:120] Balance (beta_x): 0.0539
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 171, time = 3.17 m
INFO [matcher.py:101] Objective: 128661000.0
INFO [matcher.py:120] Balance (beta_x): 0.0546
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 172, time = 3.27 m
INFO [matcher.py:101] Objective: 128657000.0
INFO [matcher.py:120] Balance (beta_x): 0.0545
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 173, time = 3.28 m
INFO [matcher.py:101] Objective: 128319000.0
INFO [matcher.py:120] Balance (beta_x): 0.0544
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 174, time = 3.46 m
INFO [matcher.py:101] Objective: 128266000.0
INFO [matcher.py:120] Balance (beta_x): 0.0543
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 175, time = 3.67 m
INFO [matcher.py:101] Objective: 127794000.0
INFO [matcher.py:120] Balance (beta_x): 0.0539
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 176, time = 3.72 m
INFO [matcher.py:101] Objective: 127703000.0
INFO [matcher.py:120] Balance (beta_x): 0.0542
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 177, time = 3.73 m
INFO [matcher.py:101] Objective: 127624000.0
INFO [matcher.py:120] Balance (beta_x): 0.0542
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 178, time = 3.77 m
INFO [matcher.py:101] Objective: 127604000.0
INFO [matcher.py:120] Balance (beta_x): 0.0542
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 179, time = 3.78 m
INFO [matcher.py:101] Objective: 127576000.0
INFO [matcher.py:120] Balance (beta_x): 0.0542
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 180, time = 3.78 m
INFO [matcher.py:101] Objective: 127565000.0
INFO [matcher.py:120] Balance (beta_x): 0.0541
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 181, time = 3.78 m
INFO [matcher.py:101] Objective: 127533000.0
INFO [matcher.py:120] Balance (beta_x): 0.0541
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 182, time = 3.78 m
INFO [matcher.py:101] Objective: 127526000.0
INFO [matcher.py:120] Balance (beta_x): 0.0541
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 183, time = 3.79 m
INFO [matcher.py:101] Objective: 127520000.0
INFO [matcher.py:120] Balance (beta_x): 0.0541
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 184, time = 3.79 m
INFO [matcher.py:101] Objective: 127464000.0
INFO [matcher.py:120] Balance (beta_x): 0.0541
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 185, time = 3.79 m
INFO [matcher.py:101] Objective: 127463000.0
INFO [matcher.py:120] Balance (beta_x): 0.0541
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 186, time = 3.82 m
INFO [matcher.py:101] Objective: 127450000.0
INFO [matcher.py:120] Balance (beta_x): 0.0541
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 187, time = 3.82 m
INFO [matcher.py:101] Objective: 127394000.0
INFO [matcher.py:120] Balance (beta_x): 0.0541
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 188, time = 3.83 m
INFO [matcher.py:101] Objective: 127388000.0
INFO [matcher.py:120] Balance (beta_x): 0.0541
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 189, time = 3.83 m
INFO [matcher.py:101] Objective: 127253000.0
INFO [matcher.py:120] Balance (beta_x): 0.0540
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 190, time = 3.84 m
INFO [matcher.py:101] Objective: 127241000.0
INFO [matcher.py:120] Balance (beta_x): 0.0540
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 191, time = 3.84 m
INFO [matcher.py:101] Objective: 127209000.0
INFO [matcher.py:120] Balance (beta_x): 0.0539
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 192, time = 3.84 m
INFO [matcher.py:101] Objective: 127189000.0
INFO [matcher.py:120] Balance (beta_x): 0.0539
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 193, time = 3.84 m
INFO [matcher.py:101] Objective: 126998000.0
INFO [matcher.py:120] Balance (beta_x): 0.0540
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 194, time = 3.86 m
INFO [matcher.py:101] Objective: 126837000.0
INFO [matcher.py:120] Balance (beta_x): 0.0539
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 195, time = 3.86 m
INFO [matcher.py:101] Objective: 126768000.0
INFO [matcher.py:120] Balance (beta_x): 0.0539
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 196, time = 3.88 m
INFO [matcher.py:101] Objective: 126686000.0
INFO [matcher.py:120] Balance (beta_x): 0.0538
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 197, time = 3.88 m
INFO [matcher.py:101] Objective: 126679000.0
INFO [matcher.py:120] Balance (beta_x): 0.0538
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 198, time = 3.89 m
INFO [matcher.py:101] Objective: 126673000.0
INFO [matcher.py:120] Balance (beta_x): 0.0538
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 199, time = 3.93 m
INFO [matcher.py:101] Objective: 126450000.0
INFO [matcher.py:120] Balance (beta_x): 0.0536
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 200, time = 4.02 m
INFO [matcher.py:101] Objective: 126359000.0
INFO [matcher.py:120] Balance (beta_x): 0.0536
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 201, time = 4.02 m
INFO [matcher.py:101] Objective: 126346000.0
INFO [matcher.py:120] Balance (beta_x): 0.0537
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 202, time = 4.02 m
INFO [matcher.py:101] Objective: 126318000.0
INFO [matcher.py:120] Balance (beta_x): 0.0536
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 203, time = 4.04 m
INFO [matcher.py:101] Objective: 125810000.0
INFO [matcher.py:120] Balance (beta_x): 0.0535
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 204, time = 4.04 m
INFO [matcher.py:101] Objective: 125799000.0
INFO [matcher.py:120] Balance (beta_x): 0.0535
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 205, time = 4.05 m
INFO [matcher.py:101] Objective: 125767000.0
INFO [matcher.py:120] Balance (beta_x): 0.0535
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 206, time = 4.05 m
INFO [matcher.py:101] Objective: 125760000.0
INFO [matcher.py:120] Balance (beta_x): 0.0534
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 207, time = 4.08 m
INFO [matcher.py:101] Objective: 125507000.0
INFO [matcher.py:120] Balance (beta_x): 0.0534
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 208, time = 4.10 m
INFO [matcher.py:101] Objective: 125496000.0
INFO [matcher.py:120] Balance (beta_x): 0.0533
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 209, time = 4.10 m
INFO [matcher.py:101] Objective: 125464000.0
INFO [matcher.py:120] Balance (beta_x): 0.0533
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 210, time = 4.15 m
INFO [matcher.py:101] Objective: 125016000.0
INFO [matcher.py:120] Balance (beta_x): 0.0532
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 211, time = 4.15 m
INFO [matcher.py:101] Objective: 124988000.0
INFO [matcher.py:120] Balance (beta_x): 0.0532
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 212, time = 4.16 m
INFO [matcher.py:101] Objective: 124956000.0
INFO [matcher.py:120] Balance (beta_x): 0.0532
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 213, time = 4.39 m
INFO [matcher.py:101] Objective: 124605000.0
INFO [matcher.py:120] Balance (beta_x): 0.0529
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 214, time = 4.57 m
INFO [matcher.py:101] Objective: 124569000.0
INFO [matcher.py:120] Balance (beta_x): 0.0530
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 215, time = 4.60 m
INFO [matcher.py:101] Objective: 124537000.0
INFO [matcher.py:120] Balance (beta_x): 0.0532
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 216, time = 4.63 m
INFO [matcher.py:101] Objective: 124416000.0
INFO [matcher.py:120] Balance (beta_x): 0.0532
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 217, time = 4.64 m
INFO [matcher.py:101] Objective: 124370000.0
INFO [matcher.py:120] Balance (beta_x): 0.0531
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 218, time = 4.66 m
INFO [matcher.py:101] Objective: 123956000.0
INFO [matcher.py:120] Balance (beta_x): 0.0530
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 219, time = 4.67 m
INFO [matcher.py:101] Objective: 123910000.0
INFO [matcher.py:120] Balance (beta_x): 0.0530
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 220, time = 4.71 m
INFO [matcher.py:101] Objective: 123851000.0
INFO [matcher.py:120] Balance (beta_x): 0.0530
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 221, time = 4.77 m
INFO [matcher.py:101] Objective: 123830000.0
INFO [matcher.py:120] Balance (beta_x): 0.0530
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 222, time = 4.77 m
INFO [matcher.py:101] Objective: 123810000.0
INFO [matcher.py:120] Balance (beta_x): 0.0530
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 223, time = 4.80 m
INFO [matcher.py:101] Objective: 123045000.0
INFO [matcher.py:120] Balance (beta_x): 0.0527
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 224, time = 4.98 m
INFO [matcher.py:101] Objective: 122999000.0
INFO [matcher.py:120] Balance (beta_x): 0.0527
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 225, time = 5.20 m
INFO [matcher.py:101] Objective: 122997000.0
INFO [matcher.py:120] Balance (beta_x): 0.0528
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 226, time = 5.20 m
INFO [matcher.py:101] Objective: 122958000.0
INFO [matcher.py:120] Balance (beta_x): 0.0528
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 227, time = 5.25 m
INFO [matcher.py:101] Objective: 122833000.0
INFO [matcher.py:120] Balance (beta_x): 0.0528
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 228, time = 5.25 m
INFO [matcher.py:101] Objective: 122802000.0
INFO [matcher.py:120] Balance (beta_x): 0.0528
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 229, time = 5.26 m
INFO [matcher.py:101] Objective: 122708000.0
INFO [matcher.py:120] Balance (beta_x): 0.0527
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 230, time = 5.30 m
INFO [matcher.py:101] Objective: 122646000.0
INFO [matcher.py:120] Balance (beta_x): 0.0527
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 231, time = 5.30 m
INFO [matcher.py:101] Objective: 122600000.0
INFO [matcher.py:120] Balance (beta_x): 0.0526
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 232, time = 5.31 m
INFO [matcher.py:101] Objective: 122464000.0
INFO [matcher.py:120] Balance (beta_x): 0.0526
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 233, time = 5.33 m
INFO [matcher.py:101] Objective: 122463000.0
INFO [matcher.py:120] Balance (beta_x): 0.0526
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 234, time = 5.34 m
INFO [matcher.py:101] Objective: 122453000.0
INFO [matcher.py:120] Balance (beta_x): 0.0526
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 235, time = 5.34 m
INFO [matcher.py:101] Objective: 122421000.0
INFO [matcher.py:120] Balance (beta_x): 0.0525
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 236, time = 5.35 m
INFO [matcher.py:101] Objective: 122333000.0
INFO [matcher.py:120] Balance (beta_x): 0.0526
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 237, time = 5.35 m
INFO [matcher.py:101] Objective: 122319000.0
INFO [matcher.py:120] Balance (beta_x): 0.0526
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 238, time = 5.35 m
INFO [matcher.py:101] Objective: 122280000.0
INFO [matcher.py:120] Balance (beta_x): 0.0526
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 239, time = 5.35 m
INFO [matcher.py:101] Objective: 122118000.0
INFO [matcher.py:120] Balance (beta_x): 0.0526
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 240, time = 5.44 m
INFO [matcher.py:101] Objective: 122093000.0
INFO [matcher.py:120] Balance (beta_x): 0.0526
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 241, time = 5.44 m
INFO [matcher.py:101] Objective: 121931000.0
INFO [matcher.py:120] Balance (beta_x): 0.0526
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 242, time = 5.44 m
INFO [matcher.py:101] Objective: 121912000.0
INFO [matcher.py:120] Balance (beta_x): 0.0526
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 243, time = 5.54 m
INFO [matcher.py:101] Objective: 121870000.0
INFO [matcher.py:120] Balance (beta_x): 0.0526
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 244, time = 5.55 m
INFO [matcher.py:101] Objective: 121820000.0
INFO [matcher.py:120] Balance (beta_x): 0.0525
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 245, time = 5.56 m
INFO [matcher.py:101] Objective: 121793000.0
INFO [matcher.py:120] Balance (beta_x): 0.0525
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 246, time = 5.58 m
INFO [matcher.py:101] Objective: 120770000.0
INFO [matcher.py:120] Balance (beta_x): 0.0521
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 247, time = 5.79 m
INFO [matcher.py:101] Objective: 120522000.0
INFO [matcher.py:120] Balance (beta_x): 0.0521
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:618] Status = FEASIBLE
INFO [matcher.py:619] Number of solutions found: 247
[9]:
Headers Numeric:
['age', 'height', 'weight']
Headers Categoric:
['gender', 'haircolor', 'country', 'binary_0', 'binary_1', 'binary_2', 'binary_3']
Populations
['pool', 'target']
['age', 'height', 'weight']
Headers Categoric:
['gender', 'haircolor', 'country', 'binary_0', 'binary_1', 'binary_2', 'binary_3']
Populations
['pool', 'target']
| age | height | weight | gender | haircolor | country | population | binary_0 | binary_1 | binary_2 | binary_3 | patient_id | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 55.261578 | 139.396134 | 94.438359 | 0.0 | 2 | 2 | target | 0 | 0 | 1 | 1 | 10000 |
| 1 | 63.113091 | 165.563337 | 67.433016 | 1.0 | 2 | 2 | target | 0 | 1 | 1 | 0 | 10001 |
| 2 | 58.232216 | 160.859857 | 71.915385 | 1.0 | 0 | 2 | target | 0 | 0 | 0 | 0 | 10002 |
| 3 | 58.996941 | 140.357415 | 115.606615 | 1.0 | 0 | 3 | target | 1 | 1 | 0 | 0 | 10003 |
| 4 | 36.850195 | 189.983706 | 53.000581 | 0.0 | 2 | 5 | target | 0 | 0 | 0 | 0 | 10004 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 9933 | 68.194783 | 127.495418 | 69.177329 | 0.0 | 1 | 5 | pool | 1 | 1 | 0 | 0 | 9933 |
| 9947 | 64.290077 | 168.091011 | 63.511962 | 1.0 | 2 | 2 | pool | 0 | 0 | 0 | 1 | 9947 |
| 9958 | 51.722321 | 170.350117 | 80.695438 | 0.0 | 2 | 4 | pool | 0 | 1 | 0 | 1 | 9958 |
| 9982 | 50.575808 | 139.401060 | 89.848616 | 0.0 | 1 | 1 | pool | 0 | 0 | 0 | 1 | 9982 |
| 9983 | 68.616093 | 167.546870 | 58.683367 | 1.0 | 0 | 2 | pool | 0 | 0 | 0 | 0 | 9983 |
2000 rows × 12 columns
[10]:
%matplotlib inline
match = matcher.get_best_match()
m_data = m.copy().get_population('pool')
m_data.loc[:, 'population'] = m_data['population'] + ' (prematch)'
match.append(m_data)
fig = plot_per_feature_loss(match, objective, 'target', debin=False)
Optimize Gamma (Area Between CDFs)
[11]:
objective = gamma = GammaBalance(m)
matcher = matcher_gamma = ConstraintSatisfactionMatcher(
m,
time_limit=time_limit,
objective=objective,
ps_hinting=True,
num_workers=4)
matcher.get_params()
INFO [preprocess.py:335] Discretized age with bins [18.05, 27.54, 37.04, 46.53, 56.02, 65.51, 75.0].
INFO [preprocess.py:335] Discretized height with bins [125.01, 136.68, 148.34, 160.01, 171.67, 183.34, 195.0].
INFO [preprocess.py:335] Discretized weight with bins [50.0, 61.67, 73.33, 85.0, 96.66, 108.33, 120.0].
INFO [matcher.py:65] Scaling features by factor 200.00 in order to use integer solver with <= 0.0000% loss.
[11]:
{'objective': 'gamma',
'pool_size': 1000,
'target_size': 1000,
'max_mismatch': None,
'time_limit': 360,
'num_workers': 4,
'ps_hinting': True,
'verbose': True}
[12]:
matcher.match()
INFO [matcher.py:418] Solving for match population with pool size = 1000 and target size = 1000 subject to None balance constraint.
INFO [matcher.py:421] Matching on 27 dimensions ...
INFO [matcher.py:428] Building model variables and constraints ...
INFO [matcher.py:437] Calculating bounds on feature variables ...
INFO [matcher.py:527] Applying size constraints on pool and target ...
INFO [matcher.py:533] Applying hint ...
INFO [matcher.py:540] Training PS model as guide for solver ...
/opt/miniconda3/envs/pybalance/lib/python3.9/site-packages/pybalance/lp/matcher.py:542: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
target.loc[:, "ix"] = list(range(len(target)))
/opt/miniconda3/envs/pybalance/lib/python3.9/site-packages/pybalance/lp/matcher.py:543: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
pool.loc[:, "ix"] = list(range(len(pool)))
INFO [preprocess.py:335] Discretized age with bins [18.05, 27.54, 37.04, 46.53, 56.02, 65.51, 75.0].
INFO [preprocess.py:335] Discretized height with bins [125.01, 136.68, 148.34, 160.01, 171.67, 183.34, 195.0].
INFO [preprocess.py:335] Discretized weight with bins [50.0, 61.67, 73.33, 85.0, 96.66, 108.33, 120.0].
INFO [matcher.py:180] Training model SGDClassifier (iter 1/50, 0.001 min) ...
INFO [matcher.py:136] Best propensity score match found:
INFO [matcher.py:137] Model: SGDClassifier
INFO [matcher.py:139] * alpha: 4.774501445415405
INFO [matcher.py:139] * class_weight: None
INFO [matcher.py:139] * early_stopping: True
INFO [matcher.py:139] * fit_intercept: False
INFO [matcher.py:139] * loss: modified_huber
INFO [matcher.py:139] * max_iter: 1500
INFO [matcher.py:139] * penalty: elasticnet
INFO [matcher.py:140] Score (gamma): 0.2142
INFO [matcher.py:141] Solution time: 0.002 min
INFO [matcher.py:180] Training model LogisticRegression (iter 2/50, 0.002 min) ...
INFO [matcher.py:136] Best propensity score match found:
INFO [matcher.py:137] Model: LogisticRegression
INFO [matcher.py:139] * C: 0.058541995189524146
INFO [matcher.py:139] * fit_intercept: False
INFO [matcher.py:139] * max_iter: 500
INFO [matcher.py:139] * penalty: l2
INFO [matcher.py:139] * solver: saga
INFO [matcher.py:140] Score (gamma): 0.0472
INFO [matcher.py:141] Solution time: 0.004 min
INFO [matcher.py:180] Training model LogisticRegression (iter 3/50, 0.004 min) ...
INFO [matcher.py:136] Best propensity score match found:
INFO [matcher.py:137] Model: LogisticRegression
INFO [matcher.py:139] * C: 0.0909955270741388
INFO [matcher.py:139] * fit_intercept: True
INFO [matcher.py:139] * max_iter: 500
INFO [matcher.py:139] * penalty: l1
INFO [matcher.py:139] * solver: saga
INFO [matcher.py:140] Score (gamma): 0.0448
INFO [matcher.py:141] Solution time: 0.005 min
INFO [matcher.py:180] Training model SGDClassifier (iter 4/50, 0.005 min) ...
INFO [matcher.py:180] Training model SGDClassifier (iter 5/50, 0.007 min) ...
INFO [matcher.py:180] Training model LogisticRegression (iter 6/50, 0.008 min) ...
/opt/miniconda3/envs/pybalance/lib/python3.9/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
warnings.warn(
INFO [matcher.py:136] Best propensity score match found:
INFO [matcher.py:137] Model: LogisticRegression
INFO [matcher.py:139] * C: 0.15085738749804528
INFO [matcher.py:139] * fit_intercept: False
INFO [matcher.py:139] * max_iter: 500
INFO [matcher.py:139] * penalty: l1
INFO [matcher.py:139] * solver: saga
INFO [matcher.py:140] Score (gamma): 0.0347
INFO [matcher.py:141] Solution time: 0.031 min
INFO [matcher.py:180] Training model SGDClassifier (iter 7/50, 0.031 min) ...
INFO [matcher.py:180] Training model LogisticRegression (iter 8/50, 0.032 min) ...
INFO [matcher.py:180] Training model LogisticRegression (iter 9/50, 0.033 min) ...
INFO [matcher.py:180] Training model LogisticRegression (iter 10/50, 0.037 min) ...
/opt/miniconda3/envs/pybalance/lib/python3.9/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
warnings.warn(
INFO [matcher.py:136] Best propensity score match found:
INFO [matcher.py:137] Model: LogisticRegression
INFO [matcher.py:139] * C: 22.70303412073022
INFO [matcher.py:139] * fit_intercept: False
INFO [matcher.py:139] * max_iter: 500
INFO [matcher.py:139] * penalty: l1
INFO [matcher.py:139] * solver: saga
INFO [matcher.py:140] Score (gamma): 0.0331
INFO [matcher.py:141] Solution time: 0.061 min
INFO [matcher.py:180] Training model LogisticRegression (iter 11/50, 0.061 min) ...
INFO [matcher.py:180] Training model LogisticRegression (iter 12/50, 0.071 min) ...
INFO [matcher.py:180] Training model LogisticRegression (iter 13/50, 0.072 min) ...
/opt/miniconda3/envs/pybalance/lib/python3.9/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
warnings.warn(
INFO [matcher.py:180] Training model LogisticRegression (iter 14/50, 0.094 min) ...
INFO [matcher.py:180] Training model SGDClassifier (iter 15/50, 0.097 min) ...
INFO [matcher.py:180] Training model SGDClassifier (iter 16/50, 0.099 min) ...
INFO [matcher.py:180] Training model LogisticRegression (iter 17/50, 0.100 min) ...
INFO [matcher.py:180] Training model SGDClassifier (iter 18/50, 0.103 min) ...
INFO [matcher.py:180] Training model LogisticRegression (iter 19/50, 0.104 min) ...
/opt/miniconda3/envs/pybalance/lib/python3.9/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
warnings.warn(
INFO [matcher.py:136] Best propensity score match found:
INFO [matcher.py:137] Model: LogisticRegression
INFO [matcher.py:139] * C: 0.7964686611607528
INFO [matcher.py:139] * fit_intercept: False
INFO [matcher.py:139] * max_iter: 500
INFO [matcher.py:139] * penalty: l1
INFO [matcher.py:139] * solver: saga
INFO [matcher.py:140] Score (gamma): 0.0308
INFO [matcher.py:141] Solution time: 0.130 min
INFO [matcher.py:180] Training model LogisticRegression (iter 20/50, 0.130 min) ...
/opt/miniconda3/envs/pybalance/lib/python3.9/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
warnings.warn(
INFO [matcher.py:180] Training model LogisticRegression (iter 21/50, 0.151 min) ...
INFO [matcher.py:180] Training model SGDClassifier (iter 22/50, 0.153 min) ...
INFO [matcher.py:180] Training model LogisticRegression (iter 23/50, 0.154 min) ...
/opt/miniconda3/envs/pybalance/lib/python3.9/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
warnings.warn(
INFO [matcher.py:180] Training model SGDClassifier (iter 24/50, 0.177 min) ...
INFO [matcher.py:180] Training model SGDClassifier (iter 25/50, 0.178 min) ...
INFO [matcher.py:180] Training model LogisticRegression (iter 26/50, 0.179 min) ...
INFO [matcher.py:180] Training model SGDClassifier (iter 27/50, 0.194 min) ...
INFO [matcher.py:180] Training model SGDClassifier (iter 28/50, 0.195 min) ...
INFO [matcher.py:180] Training model LogisticRegression (iter 29/50, 0.196 min) ...
INFO [matcher.py:180] Training model SGDClassifier (iter 30/50, 0.202 min) ...
INFO [matcher.py:180] Training model SGDClassifier (iter 31/50, 0.203 min) ...
INFO [matcher.py:180] Training model LogisticRegression (iter 32/50, 0.204 min) ...
INFO [matcher.py:180] Training model LogisticRegression (iter 33/50, 0.208 min) ...
/opt/miniconda3/envs/pybalance/lib/python3.9/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
warnings.warn(
INFO [matcher.py:180] Training model LogisticRegression (iter 34/50, 0.231 min) ...
INFO [matcher.py:180] Training model SGDClassifier (iter 35/50, 0.241 min) ...
INFO [matcher.py:180] Training model SGDClassifier (iter 36/50, 0.242 min) ...
INFO [matcher.py:180] Training model SGDClassifier (iter 37/50, 0.243 min) ...
INFO [matcher.py:180] Training model LogisticRegression (iter 38/50, 0.244 min) ...
INFO [matcher.py:180] Training model SGDClassifier (iter 39/50, 0.256 min) ...
INFO [matcher.py:180] Training model SGDClassifier (iter 40/50, 0.257 min) ...
INFO [matcher.py:180] Training model LogisticRegression (iter 41/50, 0.258 min) ...
INFO [matcher.py:180] Training model LogisticRegression (iter 42/50, 0.265 min) ...
INFO [matcher.py:180] Training model SGDClassifier (iter 43/50, 0.266 min) ...
INFO [matcher.py:180] Training model SGDClassifier (iter 44/50, 0.267 min) ...
INFO [matcher.py:180] Training model SGDClassifier (iter 45/50, 0.269 min) ...
INFO [matcher.py:180] Training model SGDClassifier (iter 46/50, 0.270 min) ...
INFO [matcher.py:180] Training model LogisticRegression (iter 47/50, 0.271 min) ...
INFO [matcher.py:180] Training model LogisticRegression (iter 48/50, 0.273 min) ...
/opt/miniconda3/envs/pybalance/lib/python3.9/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
warnings.warn(
INFO [matcher.py:180] Training model SGDClassifier (iter 49/50, 0.295 min) ...
INFO [matcher.py:180] Training model SGDClassifier (iter 50/50, 0.297 min) ...
INFO [matcher.py:136] Best propensity score match found:
INFO [matcher.py:137] Model: LogisticRegression
INFO [matcher.py:139] * C: 0.7964686611607528
INFO [matcher.py:139] * fit_intercept: False
INFO [matcher.py:139] * max_iter: 500
INFO [matcher.py:139] * penalty: l1
INFO [matcher.py:139] * solver: saga
INFO [matcher.py:140] Score (gamma): 0.0308
INFO [matcher.py:141] Solution time: 0.130 min
INFO [matcher.py:577] Hint achieves objective value = 100400.
INFO [matcher.py:579] Applying hints ...
INFO [matcher.py:611] Solving with 4 workers ...
INFO [matcher.py:90] Initial balance score: 0.2110
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 1, time = 0.05 m
INFO [matcher.py:101] Objective: 115600000.0
INFO [matcher.py:120] Balance (gamma): 0.0349
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 2, time = 0.13 m
INFO [matcher.py:101] Objective: 115400000.0
INFO [matcher.py:120] Balance (gamma): 0.0349
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 3, time = 0.14 m
INFO [matcher.py:101] Objective: 114600000.0
INFO [matcher.py:120] Balance (gamma): 0.0346
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 4, time = 0.14 m
INFO [matcher.py:101] Objective: 114400000.0
INFO [matcher.py:120] Balance (gamma): 0.0346
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 5, time = 0.16 m
INFO [matcher.py:101] Objective: 113800000.0
INFO [matcher.py:120] Balance (gamma): 0.0344
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 6, time = 0.17 m
INFO [matcher.py:101] Objective: 102000000.0
INFO [matcher.py:120] Balance (gamma): 0.0310
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 7, time = 0.17 m
INFO [matcher.py:101] Objective: 101400000.0
INFO [matcher.py:120] Balance (gamma): 0.0308
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 8, time = 0.19 m
INFO [matcher.py:101] Objective: 100800000.0
INFO [matcher.py:120] Balance (gamma): 0.0306
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 9, time = 0.20 m
INFO [matcher.py:101] Objective: 100400000.0
INFO [matcher.py:120] Balance (gamma): 0.0305
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 10, time = 0.26 m
INFO [matcher.py:101] Objective: 97800000.0
INFO [matcher.py:120] Balance (gamma): 0.0300
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 11, time = 0.26 m
INFO [matcher.py:101] Objective: 97200000.0
INFO [matcher.py:120] Balance (gamma): 0.0298
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 12, time = 0.31 m
INFO [matcher.py:101] Objective: 97000000.0
INFO [matcher.py:120] Balance (gamma): 0.0298
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 13, time = 0.32 m
INFO [matcher.py:101] Objective: 96600000.0
INFO [matcher.py:120] Balance (gamma): 0.0297
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 14, time = 0.37 m
INFO [matcher.py:101] Objective: 91000000.0
INFO [matcher.py:120] Balance (gamma): 0.0282
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 15, time = 0.37 m
INFO [matcher.py:101] Objective: 90800000.0
INFO [matcher.py:120] Balance (gamma): 0.0282
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 16, time = 0.38 m
INFO [matcher.py:101] Objective: 90600000.0
INFO [matcher.py:120] Balance (gamma): 0.0281
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 17, time = 0.42 m
INFO [matcher.py:101] Objective: 80800000.0
INFO [matcher.py:120] Balance (gamma): 0.0249
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 18, time = 0.43 m
INFO [matcher.py:101] Objective: 80600000.0
INFO [matcher.py:120] Balance (gamma): 0.0248
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 19, time = 0.44 m
INFO [matcher.py:101] Objective: 80400000.0
INFO [matcher.py:120] Balance (gamma): 0.0247
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 20, time = 0.48 m
INFO [matcher.py:101] Objective: 19000000.0
INFO [matcher.py:120] Balance (gamma): 0.0070
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 21, time = 0.64 m
INFO [matcher.py:101] Objective: 18800000.0
INFO [matcher.py:120] Balance (gamma): 0.0069
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:96] =========================================
INFO [matcher.py:97] Solution 22, time = 0.66 m
INFO [matcher.py:101] Objective: 18400000.0
INFO [matcher.py:120] Balance (gamma): 0.0068
INFO [matcher.py:125] Patients (pool): 1000
INFO [matcher.py:126] Patients (target): 1000
INFO [matcher.py:140]
INFO [matcher.py:618] Status = OPTIMAL
INFO [matcher.py:619] Number of solutions found: 22
[12]:
Headers Numeric:
['age', 'height', 'weight']
Headers Categoric:
['gender', 'haircolor', 'country', 'binary_0', 'binary_1', 'binary_2', 'binary_3']
Populations
['pool', 'target']
['age', 'height', 'weight']
Headers Categoric:
['gender', 'haircolor', 'country', 'binary_0', 'binary_1', 'binary_2', 'binary_3']
Populations
['pool', 'target']
| age | height | weight | gender | haircolor | country | population | binary_0 | binary_1 | binary_2 | binary_3 | patient_id | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 55.261578 | 139.396134 | 94.438359 | 0.0 | 2 | 2 | target | 0 | 0 | 1 | 1 | 10000 |
| 1 | 63.113091 | 165.563337 | 67.433016 | 1.0 | 2 | 2 | target | 0 | 1 | 1 | 0 | 10001 |
| 2 | 58.232216 | 160.859857 | 71.915385 | 1.0 | 0 | 2 | target | 0 | 0 | 0 | 0 | 10002 |
| 3 | 58.996941 | 140.357415 | 115.606615 | 1.0 | 0 | 3 | target | 1 | 1 | 0 | 0 | 10003 |
| 4 | 36.850195 | 189.983706 | 53.000581 | 0.0 | 2 | 5 | target | 0 | 0 | 0 | 0 | 10004 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 9932 | 72.514956 | 159.248205 | 118.505187 | 0.0 | 1 | 5 | pool | 1 | 1 | 0 | 1 | 9932 |
| 9933 | 68.194783 | 127.495418 | 69.177329 | 0.0 | 1 | 5 | pool | 1 | 1 | 0 | 0 | 9933 |
| 9965 | 41.035792 | 130.021437 | 80.495109 | 0.0 | 0 | 1 | pool | 0 | 1 | 1 | 1 | 9965 |
| 9966 | 40.121009 | 168.339212 | 100.428001 | 1.0 | 2 | 4 | pool | 0 | 1 | 1 | 0 | 9966 |
| 9984 | 57.366166 | 151.483411 | 82.271539 | 1.0 | 2 | 2 | pool | 0 | 1 | 0 | 1 | 9984 |
2000 rows × 12 columns
[13]:
%matplotlib inline
match = matcher.get_best_match()
m_data = m.copy().get_population('pool')
m_data.loc[:, 'population'] = m_data['population'] + ' (prematch)'
match.append(m_data)
fig = plot_per_feature_loss(match, objective, 'target', debin=False)
fig = plot_numeric_features(match, hue_order=['pool (prematch)', 'pool', 'target', ])
fig = plot_categoric_features(match, hue_order=['pool (prematch)', 'pool', 'target'])
