In [5]:
%load_ext autoreload
%autoreload 2
import os, sys
sys.path.append('..')
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_context("poster")
import numpy as np
from sklearn.ensemble import RandomForestRegressor
import pandas as pd
import matplotlib as mpl
mpl.rcParams['figure.figsize'] = (11,8)
from merf.utils import MERFDataGenerator
from merf.merf import MERF
There are some global parameters for all the experiments. Each experiment is run N_per_experiment times. The experiment itself is parametrized by three parameters of the generative model. We collect up the results of the experiments in a big list of dictinaries. This is then used to compute certain summary statistics after all the experiments are over.
In [107]:
# Globals
num_clusters_each_size = 20
train_sizes = [1, 3, 5, 7, 9]
known_sizes = [9, 27, 45, 63, 81]
new_sizes = [10, 30, 50, 70, 90]
n_estimators = 300
max_iterations = 100
train_cluster_sizes = MERFDataGenerator.create_cluster_sizes_array(train_sizes, num_clusters_each_size)
known_cluster_sizes = MERFDataGenerator.create_cluster_sizes_array(known_sizes, num_clusters_each_size)
new_cluster_sizes = MERFDataGenerator.create_cluster_sizes_array(new_sizes, num_clusters_each_size)
In [108]:
# Number of times to run each experiemnts
N_per_experiment = 10
In [8]:
# Defining the experiments to run
experiments = [{'id': 0, 'm': .8, 'sigma_b_sq': 0.9, 'sigma_e': 1},
{'id': 1, 'm': .7, 'sigma_b_sq': 2.7, 'sigma_e': 1},
{'id': 2, 'm': .6, 'sigma_b_sq': 4.5, 'sigma_e': 1},
{'id': 3, 'm': .3, 'sigma_b_sq': 0.2, 'sigma_e': 1},
{'id': 4, 'm': .3, 'sigma_b_sq': 0.5, 'sigma_e': 1},
{'id': 5, 'm': .2, 'sigma_b_sq': 0.8, 'sigma_e': 1}]
In [110]:
# Creating a dictionary to hold the results of the experiments
results = []
for experiment in experiments:
results.append({'id': experiment['id'], 'ptev': [], 'prev': [],
'mse_known_rf_fixed': [], 'mse_known_rf_ohe': [], 'mse_known_merf': [],
'mse_new_rf_fixed': [], 'mse_new_rf_ohe': [], 'mse_new_merf': []})
for experiment, result in zip(experiments, results):
for experiment_iteration in range(0, N_per_experiment):
print("Experiment iteration: {}".format(experiment_iteration))
# Generate data for experiment
dgm = MERFDataGenerator(m=experiment['m'], sigma_b=np.sqrt(experiment['sigma_b_sq']), sigma_e=experiment['sigma_e'])
train, test_known, test_new, train_cluster_ids, ptev, prev = dgm.generate_split_samples(train_cluster_sizes, known_cluster_sizes, new_cluster_sizes)
# Store off PTEV and PREV
result['ptev'].append(ptev)
result['prev'].append(prev)
# Training Data Extract
X_train = train[['X_0', 'X_1', 'X_2']]
Z_train = train[['Z']]
clusters_train = train['cluster']
y_train = train['y']
# Known Cluster Data Extract
X_known = test_known[['X_0', 'X_1', 'X_2']]
Z_known = test_known[['Z']]
clusters_known = test_known['cluster']
y_known = test_known['y']
# New Cluster Data Extract
X_new = test_new[['X_0', 'X_1', 'X_2']]
Z_new = test_new[['Z']]
clusters_new = test_new['cluster']
y_new = test_new['y']
# MERF
print("---------------------MERF----------------------")
mrf = MERF(n_estimators=n_estimators, max_iterations=max_iterations)
mrf.fit(X_train, Z_train, clusters_train, y_train)
y_hat_known_merf = mrf.predict(X_known, Z_known, clusters_known)
y_hat_new_merf = mrf.predict(X_new, Z_new, clusters_new)
mse_known_merf = np.mean((y_known - y_hat_known_merf) ** 2)
mse_new_merf = np.mean((y_new - y_hat_new_merf) ** 2)
result['mse_known_merf'].append(mse_known_merf)
result['mse_new_merf'].append(mse_new_merf)
# Random Forest Fixed Only
print("---------------------Random Forest Fixed Effect Only----------------------")
rf = RandomForestRegressor(n_estimators=n_estimators, n_jobs=-1)
rf.fit(X_train, y_train)
y_hat_known_rf_fixed = rf.predict(X_known)
y_hat_new_rf_fixed = rf.predict(X_new)
mse_known_rf_fixed = np.mean((y_known - y_hat_known_rf_fixed) ** 2)
mse_new_rf_fixed = np.mean((y_new - y_hat_new_rf_fixed) ** 2)
result['mse_known_rf_fixed'].append(mse_known_rf_fixed)
result['mse_new_rf_fixed'].append(mse_new_rf_fixed)
# Random Forest with OHE Cluster
print("---------------------Random Forest w OHE Cluster----------------------")
X_train_w_ohe = MERFDataGenerator.create_X_with_ohe_clusters(X_train, clusters_train, train_cluster_ids)
X_known_w_ohe = MERFDataGenerator.create_X_with_ohe_clusters(X_known, clusters_known, train_cluster_ids)
X_new_w_ohe = MERFDataGenerator.create_X_with_ohe_clusters(X_new, clusters_new, train_cluster_ids)
rf_ohe = RandomForestRegressor(n_estimators=n_estimators, n_jobs=-1)
rf_ohe.fit(X_train_w_ohe, y_train)
y_hat_known_rf_ohe = rf_ohe.predict(X_known_w_ohe)
y_hat_new_rf_ohe = rf_ohe.predict(X_new_w_ohe)
mse_known_rf_ohe = np.mean((y_known - y_hat_known_rf_ohe) ** 2)
mse_new_rf_ohe = np.mean((y_new - y_hat_new_rf_ohe) ** 2)
result['mse_known_rf_ohe'].append(mse_known_rf_ohe)
result['mse_new_rf_ohe'].append(mse_new_rf_ohe)
INFO [utils.py:164] Drew 10000 samples from 200 clusters.
INFO [utils.py:165] PTEV = 89.71798312729888, PREV = 10.31433706250505.
Experiment iteration: 0
---------------------MERF----------------------
INFO [merf.py:235] GLL is 817.2084771785458 at iteration 1.
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---------------------Random Forest Fixed Effect Only----------------------
---------------------Random Forest w OHE Cluster----------------------
/Users/souravdey/.virtualenvs/merf/lib/python3.6/site-packages/pandas/core/internals.py:490: RuntimeWarning: None of the categories were found in values. Did you mean to use
'Categorical.from_codes(codes, categories)'?
return self.make_block(Categorical(self.values, **kwargs))
INFO [utils.py:164] Drew 10000 samples from 200 clusters.
INFO [utils.py:165] PTEV = 89.82845754059677, PREV = 10.190966720457933.
Experiment iteration: 1
---------------------MERF----------------------
INFO [merf.py:235] GLL is 1112.692896063652 at iteration 1.
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---------------------Random Forest Fixed Effect Only----------------------
---------------------Random Forest w OHE Cluster----------------------
/Users/souravdey/.virtualenvs/merf/lib/python3.6/site-packages/pandas/core/internals.py:490: RuntimeWarning: None of the categories were found in values. Did you mean to use
'Categorical.from_codes(codes, categories)'?
return self.make_block(Categorical(self.values, **kwargs))
INFO [utils.py:164] Drew 10000 samples from 200 clusters.
INFO [utils.py:165] PTEV = 90.14705273936468, PREV = 9.836874601114422.
Experiment iteration: 2
---------------------MERF----------------------
INFO [merf.py:235] GLL is 1012.4500230784279 at iteration 1.
INFO [merf.py:235] GLL is 988.6995963928698 at iteration 2.
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INFO [merf.py:235] GLL is 1001.8206104652139 at iteration 91.
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INFO [merf.py:235] GLL is 986.9649548783732 at iteration 100.
---------------------Random Forest Fixed Effect Only----------------------
---------------------Random Forest w OHE Cluster----------------------
/Users/souravdey/.virtualenvs/merf/lib/python3.6/site-packages/pandas/core/internals.py:490: RuntimeWarning: None of the categories were found in values. Did you mean to use
'Categorical.from_codes(codes, categories)'?
return self.make_block(Categorical(self.values, **kwargs))
INFO [utils.py:164] Drew 10000 samples from 200 clusters.
INFO [utils.py:165] PTEV = 90.05454483697068, PREV = 9.939431333455273.
Experiment iteration: 3
---------------------MERF----------------------
INFO [merf.py:235] GLL is 981.5397617779056 at iteration 1.
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INFO [merf.py:235] GLL is 919.137419394581 at iteration 100.
---------------------Random Forest Fixed Effect Only----------------------
---------------------Random Forest w OHE Cluster----------------------
/Users/souravdey/.virtualenvs/merf/lib/python3.6/site-packages/pandas/core/internals.py:490: RuntimeWarning: None of the categories were found in values. Did you mean to use
'Categorical.from_codes(codes, categories)'?
return self.make_block(Categorical(self.values, **kwargs))
INFO [utils.py:164] Drew 10000 samples from 200 clusters.
INFO [utils.py:165] PTEV = 89.9154591373451, PREV = 10.094022611312898.
Experiment iteration: 4
---------------------MERF----------------------
INFO [merf.py:235] GLL is 876.8384430060706 at iteration 1.
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INFO [merf.py:235] GLL is 828.9385861710225 at iteration 100.
---------------------Random Forest Fixed Effect Only----------------------
---------------------Random Forest w OHE Cluster----------------------
/Users/souravdey/.virtualenvs/merf/lib/python3.6/site-packages/pandas/core/internals.py:490: RuntimeWarning: None of the categories were found in values. Did you mean to use
'Categorical.from_codes(codes, categories)'?
return self.make_block(Categorical(self.values, **kwargs))
INFO [utils.py:164] Drew 10000 samples from 200 clusters.
INFO [utils.py:165] PTEV = 89.87532740428294, PREV = 10.138717264590582.
Experiment iteration: 5
---------------------MERF----------------------
INFO [merf.py:235] GLL is 1018.4139152152457 at iteration 1.
INFO [merf.py:235] GLL is 906.4426567576744 at iteration 2.
INFO [merf.py:235] GLL is 919.5877962896103 at iteration 3.
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INFO [merf.py:235] GLL is 935.212286226937 at iteration 100.
---------------------Random Forest Fixed Effect Only----------------------
---------------------Random Forest w OHE Cluster----------------------
/Users/souravdey/.virtualenvs/merf/lib/python3.6/site-packages/pandas/core/internals.py:490: RuntimeWarning: None of the categories were found in values. Did you mean to use
'Categorical.from_codes(codes, categories)'?
return self.make_block(Categorical(self.values, **kwargs))
INFO [utils.py:164] Drew 10000 samples from 200 clusters.
INFO [utils.py:165] PTEV = 89.78303996360474, PREV = 10.241649242978637.
Experiment iteration: 6
---------------------MERF----------------------
INFO [merf.py:235] GLL is 916.7406404160876 at iteration 1.
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INFO [merf.py:235] GLL is 861.8854696961661 at iteration 100.
---------------------Random Forest Fixed Effect Only----------------------
---------------------Random Forest w OHE Cluster----------------------
/Users/souravdey/.virtualenvs/merf/lib/python3.6/site-packages/pandas/core/internals.py:490: RuntimeWarning: None of the categories were found in values. Did you mean to use
'Categorical.from_codes(codes, categories)'?
return self.make_block(Categorical(self.values, **kwargs))
INFO [utils.py:164] Drew 10000 samples from 200 clusters.
INFO [utils.py:165] PTEV = 90.13775444665501, PREV = 9.847173420837173.
Experiment iteration: 7
---------------------MERF----------------------
INFO [merf.py:235] GLL is 1261.4154918094719 at iteration 1.
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INFO [merf.py:235] GLL is 1197.2286684577825 at iteration 96.
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INFO [merf.py:235] GLL is 1207.1477069010703 at iteration 98.
INFO [merf.py:235] GLL is 1190.875563486595 at iteration 99.
INFO [merf.py:235] GLL is 1196.558043276275 at iteration 100.
---------------------Random Forest Fixed Effect Only----------------------
---------------------Random Forest w OHE Cluster----------------------
/Users/souravdey/.virtualenvs/merf/lib/python3.6/site-packages/pandas/core/internals.py:490: RuntimeWarning: None of the categories were found in values. Did you mean to use
'Categorical.from_codes(codes, categories)'?
return self.make_block(Categorical(self.values, **kwargs))
INFO [utils.py:164] Drew 10000 samples from 200 clusters.
INFO [utils.py:165] PTEV = 89.72327745877637, PREV = 10.30841778082703.
Experiment iteration: 8
---------------------MERF----------------------
INFO [merf.py:235] GLL is 976.6838441966427 at iteration 1.
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INFO [merf.py:235] GLL is 931.5375913281789 at iteration 99.
INFO [merf.py:235] GLL is 929.1045527153344 at iteration 100.
---------------------Random Forest Fixed Effect Only----------------------
---------------------Random Forest w OHE Cluster----------------------
/Users/souravdey/.virtualenvs/merf/lib/python3.6/site-packages/pandas/core/internals.py:490: RuntimeWarning: None of the categories were found in values. Did you mean to use
'Categorical.from_codes(codes, categories)'?
return self.make_block(Categorical(self.values, **kwargs))
INFO [utils.py:164] Drew 10000 samples from 200 clusters.
INFO [utils.py:165] PTEV = 89.92004770682647, PREV = 10.088914869611958.
Experiment iteration: 9
---------------------MERF----------------------
INFO [merf.py:235] GLL is 1018.0538748705791 at iteration 1.
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INFO [merf.py:235] GLL is 968.626065953553 at iteration 100.
---------------------Random Forest Fixed Effect Only----------------------
---------------------Random Forest w OHE Cluster----------------------
/Users/souravdey/.virtualenvs/merf/lib/python3.6/site-packages/pandas/core/internals.py:490: RuntimeWarning: None of the categories were found in values. Did you mean to use
'Categorical.from_codes(codes, categories)'?
return self.make_block(Categorical(self.values, **kwargs))
INFO [utils.py:164] Drew 10000 samples from 200 clusters.
INFO [utils.py:165] PTEV = 89.7364743716281, PREV = 30.8809983795905.
Experiment iteration: 0
---------------------MERF----------------------
INFO [merf.py:235] GLL is 1047.6472235945714 at iteration 1.
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INFO [merf.py:235] GLL is 908.1347928668022 at iteration 99.
INFO [merf.py:235] GLL is 910.117145907576 at iteration 100.
---------------------Random Forest Fixed Effect Only----------------------
---------------------Random Forest w OHE Cluster----------------------
/Users/souravdey/.virtualenvs/merf/lib/python3.6/site-packages/pandas/core/internals.py:490: RuntimeWarning: None of the categories were found in values. Did you mean to use
'Categorical.from_codes(codes, categories)'?
return self.make_block(Categorical(self.values, **kwargs))
INFO [utils.py:164] Drew 10000 samples from 200 clusters.
INFO [utils.py:165] PTEV = 89.94961027747115, PREV = 30.168059836079607.
Experiment iteration: 1
---------------------MERF----------------------
INFO [merf.py:235] GLL is 1002.57927039639 at iteration 1.
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INFO [merf.py:235] GLL is 946.8592788953492 at iteration 99.
INFO [merf.py:235] GLL is 957.8219496411057 at iteration 100.
---------------------Random Forest Fixed Effect Only----------------------
---------------------Random Forest w OHE Cluster----------------------
/Users/souravdey/.virtualenvs/merf/lib/python3.6/site-packages/pandas/core/internals.py:490: RuntimeWarning: None of the categories were found in values. Did you mean to use
'Categorical.from_codes(codes, categories)'?
return self.make_block(Categorical(self.values, **kwargs))
INFO [utils.py:164] Drew 10000 samples from 200 clusters.
INFO [utils.py:165] PTEV = 90.06621832894174, PREV = 29.77943451994434.
Experiment iteration: 2
---------------------MERF----------------------
INFO [merf.py:235] GLL is 1025.5378458176838 at iteration 1.
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INFO [merf.py:235] GLL is 969.2453976785984 at iteration 100.
---------------------Random Forest Fixed Effect Only----------------------
---------------------Random Forest w OHE Cluster----------------------
/Users/souravdey/.virtualenvs/merf/lib/python3.6/site-packages/pandas/core/internals.py:490: RuntimeWarning: None of the categories were found in values. Did you mean to use
'Categorical.from_codes(codes, categories)'?
return self.make_block(Categorical(self.values, **kwargs))
INFO [utils.py:164] Drew 10000 samples from 200 clusters.
INFO [utils.py:165] PTEV = 89.73715315868779, PREV = 30.87872246464316.
Experiment iteration: 3
---------------------MERF----------------------
INFO [merf.py:235] GLL is 983.7678599611345 at iteration 1.
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INFO [merf.py:235] GLL is 855.56661972637 at iteration 99.
INFO [merf.py:235] GLL is 855.0889391760581 at iteration 100.
---------------------Random Forest Fixed Effect Only----------------------
---------------------Random Forest w OHE Cluster----------------------
/Users/souravdey/.virtualenvs/merf/lib/python3.6/site-packages/pandas/core/internals.py:490: RuntimeWarning: None of the categories were found in values. Did you mean to use
'Categorical.from_codes(codes, categories)'?
return self.make_block(Categorical(self.values, **kwargs))
INFO [utils.py:164] Drew 10000 samples from 200 clusters.
INFO [utils.py:165] PTEV = 89.96118496816533, PREV = 30.129439263772767.
Experiment iteration: 4
---------------------MERF----------------------
INFO [merf.py:235] GLL is 1145.8786838484693 at iteration 1.
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INFO [merf.py:235] GLL is 1043.2331712029259 at iteration 99.
INFO [merf.py:235] GLL is 1038.2581506583906 at iteration 100.
---------------------Random Forest Fixed Effect Only----------------------
---------------------Random Forest w OHE Cluster----------------------
/Users/souravdey/.virtualenvs/merf/lib/python3.6/site-packages/pandas/core/internals.py:490: RuntimeWarning: None of the categories were found in values. Did you mean to use
'Categorical.from_codes(codes, categories)'?
return self.make_block(Categorical(self.values, **kwargs))
INFO [utils.py:164] Drew 10000 samples from 200 clusters.
INFO [utils.py:165] PTEV = 89.53505314401171, PREV = 31.557870933210193.
Experiment iteration: 5
---------------------MERF----------------------
INFO [merf.py:235] GLL is 1003.8007343877524 at iteration 1.
INFO [merf.py:235] GLL is 888.78685430947 at iteration 2.
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INFO [merf.py:235] GLL is 906.506100293357 at iteration 100.
---------------------Random Forest Fixed Effect Only----------------------
---------------------Random Forest w OHE Cluster----------------------
/Users/souravdey/.virtualenvs/merf/lib/python3.6/site-packages/pandas/core/internals.py:490: RuntimeWarning: None of the categories were found in values. Did you mean to use
'Categorical.from_codes(codes, categories)'?
return self.make_block(Categorical(self.values, **kwargs))
INFO [utils.py:164] Drew 10000 samples from 200 clusters.
INFO [utils.py:165] PTEV = 89.76466125689862, PREV = 30.786519126144302.
Experiment iteration: 6
---------------------MERF----------------------
INFO [merf.py:235] GLL is 1100.5856328418215 at iteration 1.
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INFO [merf.py:235] GLL is 1049.46634947037 at iteration 100.
---------------------Random Forest Fixed Effect Only----------------------
---------------------Random Forest w OHE Cluster----------------------
/Users/souravdey/.virtualenvs/merf/lib/python3.6/site-packages/pandas/core/internals.py:490: RuntimeWarning: None of the categories were found in values. Did you mean to use
'Categorical.from_codes(codes, categories)'?
return self.make_block(Categorical(self.values, **kwargs))
INFO [utils.py:164] Drew 10000 samples from 200 clusters.
INFO [utils.py:165] PTEV = 89.38585415464289, PREV = 32.061218246998926.
Experiment iteration: 7
---------------------MERF----------------------
INFO [merf.py:235] GLL is 1100.6887308282298 at iteration 1.
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INFO [merf.py:235] GLL is 1004.4865257614512 at iteration 99.
INFO [merf.py:235] GLL is 993.461182362367 at iteration 100.
---------------------Random Forest Fixed Effect Only----------------------
---------------------Random Forest w OHE Cluster----------------------
/Users/souravdey/.virtualenvs/merf/lib/python3.6/site-packages/pandas/core/internals.py:490: RuntimeWarning: None of the categories were found in values. Did you mean to use
'Categorical.from_codes(codes, categories)'?
return self.make_block(Categorical(self.values, **kwargs))
INFO [utils.py:164] Drew 10000 samples from 200 clusters.
INFO [utils.py:165] PTEV = 89.64755246074705, PREV = 31.179443932082624.
Experiment iteration: 8
---------------------MERF----------------------
INFO [merf.py:235] GLL is 1000.3268672792307 at iteration 1.
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INFO [merf.py:235] GLL is 919.3221926762003 at iteration 100.
---------------------Random Forest Fixed Effect Only----------------------
---------------------Random Forest w OHE Cluster----------------------
/Users/souravdey/.virtualenvs/merf/lib/python3.6/site-packages/pandas/core/internals.py:490: RuntimeWarning: None of the categories were found in values. Did you mean to use
'Categorical.from_codes(codes, categories)'?
return self.make_block(Categorical(self.values, **kwargs))
INFO [utils.py:164] Drew 10000 samples from 200 clusters.
INFO [utils.py:165] PTEV = 89.52414415358938, PREV = 31.594617354601784.
Experiment iteration: 9
---------------------MERF----------------------
INFO [merf.py:235] GLL is 1152.625743875798 at iteration 1.
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INFO [merf.py:235] GLL is 1003.1885043362255 at iteration 100.
---------------------Random Forest Fixed Effect Only----------------------
---------------------Random Forest w OHE Cluster----------------------
/Users/souravdey/.virtualenvs/merf/lib/python3.6/site-packages/pandas/core/internals.py:490: RuntimeWarning: None of the categories were found in values. Did you mean to use
'Categorical.from_codes(codes, categories)'?
return self.make_block(Categorical(self.values, **kwargs))
INFO [utils.py:164] Drew 10000 samples from 200 clusters.
INFO [utils.py:165] PTEV = 89.83893143575852, PREV = 50.89642965286518.
Experiment iteration: 0
---------------------MERF----------------------
INFO [merf.py:235] GLL is 1285.648037267356 at iteration 1.
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INFO [merf.py:235] GLL is 1011.677739102058 at iteration 100.
---------------------Random Forest Fixed Effect Only----------------------
---------------------Random Forest w OHE Cluster----------------------
/Users/souravdey/.virtualenvs/merf/lib/python3.6/site-packages/pandas/core/internals.py:490: RuntimeWarning: None of the categories were found in values. Did you mean to use
'Categorical.from_codes(codes, categories)'?
return self.make_block(Categorical(self.values, **kwargs))
INFO [utils.py:164] Drew 10000 samples from 200 clusters.
INFO [utils.py:165] PTEV = 89.92928572793377, PREV = 50.39316598310454.
Experiment iteration: 1
---------------------MERF----------------------
INFO [merf.py:235] GLL is 1120.3386731155008 at iteration 1.
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INFO [merf.py:235] GLL is 927.5673171670961 at iteration 100.
---------------------Random Forest Fixed Effect Only----------------------
---------------------Random Forest w OHE Cluster----------------------
/Users/souravdey/.virtualenvs/merf/lib/python3.6/site-packages/pandas/core/internals.py:490: RuntimeWarning: None of the categories were found in values. Did you mean to use
'Categorical.from_codes(codes, categories)'?
return self.make_block(Categorical(self.values, **kwargs))
INFO [utils.py:164] Drew 10000 samples from 200 clusters.
INFO [utils.py:165] PTEV = 89.96340791401448, PREV = 50.203372053338.
Experiment iteration: 2
---------------------MERF----------------------
INFO [merf.py:235] GLL is 1093.468186750158 at iteration 1.
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INFO [merf.py:235] GLL is 911.9597482876236 at iteration 100.
---------------------Random Forest Fixed Effect Only----------------------
---------------------Random Forest w OHE Cluster----------------------
/Users/souravdey/.virtualenvs/merf/lib/python3.6/site-packages/pandas/core/internals.py:490: RuntimeWarning: None of the categories were found in values. Did you mean to use
'Categorical.from_codes(codes, categories)'?
return self.make_block(Categorical(self.values, **kwargs))
INFO [utils.py:164] Drew 10000 samples from 200 clusters.
INFO [utils.py:165] PTEV = 90.05429133099851, PREV = 49.698563332207186.
Experiment iteration: 3
---------------------MERF----------------------
INFO [merf.py:235] GLL is 1053.5093536518752 at iteration 1.
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INFO [merf.py:235] GLL is 819.2979504822968 at iteration 99.
INFO [merf.py:235] GLL is 805.3330648929881 at iteration 100.
---------------------Random Forest Fixed Effect Only----------------------
---------------------Random Forest w OHE Cluster----------------------
/Users/souravdey/.virtualenvs/merf/lib/python3.6/site-packages/pandas/core/internals.py:490: RuntimeWarning: None of the categories were found in values. Did you mean to use
'Categorical.from_codes(codes, categories)'?
return self.make_block(Categorical(self.values, **kwargs))
INFO [utils.py:164] Drew 10000 samples from 200 clusters.
INFO [utils.py:165] PTEV = 89.90606979501028, PREV = 50.52237966359717.
Experiment iteration: 4
---------------------MERF----------------------
INFO [merf.py:235] GLL is 1158.0880866402554 at iteration 1.
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INFO [merf.py:235] GLL is 974.3500618153221 at iteration 100.
---------------------Random Forest Fixed Effect Only----------------------
---------------------Random Forest w OHE Cluster----------------------
/Users/souravdey/.virtualenvs/merf/lib/python3.6/site-packages/pandas/core/internals.py:490: RuntimeWarning: None of the categories were found in values. Did you mean to use
'Categorical.from_codes(codes, categories)'?
return self.make_block(Categorical(self.values, **kwargs))
INFO [utils.py:164] Drew 10000 samples from 200 clusters.
INFO [utils.py:165] PTEV = 90.03843245230826, PREV = 49.78657751328235.
Experiment iteration: 5
---------------------MERF----------------------
INFO [merf.py:235] GLL is 1174.32287390002 at iteration 1.
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INFO [merf.py:235] GLL is 950.5835834588224 at iteration 100.
---------------------Random Forest Fixed Effect Only----------------------
---------------------Random Forest w OHE Cluster----------------------
/Users/souravdey/.virtualenvs/merf/lib/python3.6/site-packages/pandas/core/internals.py:490: RuntimeWarning: None of the categories were found in values. Did you mean to use
'Categorical.from_codes(codes, categories)'?
return self.make_block(Categorical(self.values, **kwargs))
INFO [utils.py:164] Drew 10000 samples from 200 clusters.
INFO [utils.py:165] PTEV = 89.78850565060533, PREV = 51.17773621390727.
Experiment iteration: 6
---------------------MERF----------------------
INFO [merf.py:235] GLL is 1116.8445834145784 at iteration 1.
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INFO [merf.py:235] GLL is 913.9762746016215 at iteration 100.
---------------------Random Forest Fixed Effect Only----------------------
---------------------Random Forest w OHE Cluster----------------------
/Users/souravdey/.virtualenvs/merf/lib/python3.6/site-packages/pandas/core/internals.py:490: RuntimeWarning: None of the categories were found in values. Did you mean to use
'Categorical.from_codes(codes, categories)'?
return self.make_block(Categorical(self.values, **kwargs))
INFO [utils.py:164] Drew 10000 samples from 200 clusters.
INFO [utils.py:165] PTEV = 90.01216428796613, PREV = 49.93242975512055.
Experiment iteration: 7
---------------------MERF----------------------
INFO [merf.py:235] GLL is 1097.088428373426 at iteration 1.
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INFO [merf.py:235] GLL is 936.5998718964067 at iteration 100.
---------------------Random Forest Fixed Effect Only----------------------
---------------------Random Forest w OHE Cluster----------------------
/Users/souravdey/.virtualenvs/merf/lib/python3.6/site-packages/pandas/core/internals.py:490: RuntimeWarning: None of the categories were found in values. Did you mean to use
'Categorical.from_codes(codes, categories)'?
return self.make_block(Categorical(self.values, **kwargs))
INFO [utils.py:164] Drew 10000 samples from 200 clusters.
INFO [utils.py:165] PTEV = 90.00490654125586, PREV = 49.972742923444805.
Experiment iteration: 8
---------------------MERF----------------------
INFO [merf.py:235] GLL is 1079.9170682256984 at iteration 1.
INFO [merf.py:235] GLL is 889.3313806180043 at iteration 2.
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INFO [merf.py:235] GLL is 870.0003195469205 at iteration 6.
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INFO [merf.py:235] GLL is 891.7196895287113 at iteration 100.
---------------------Random Forest Fixed Effect Only----------------------
---------------------Random Forest w OHE Cluster----------------------
/Users/souravdey/.virtualenvs/merf/lib/python3.6/site-packages/pandas/core/internals.py:490: RuntimeWarning: None of the categories were found in values. Did you mean to use
'Categorical.from_codes(codes, categories)'?
return self.make_block(Categorical(self.values, **kwargs))
INFO [utils.py:164] Drew 10000 samples from 200 clusters.
INFO [utils.py:165] PTEV = 89.8231914278205, PREV = 50.984203351990544.
Experiment iteration: 9
---------------------MERF----------------------
INFO [merf.py:235] GLL is 1188.6205848070124 at iteration 1.
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INFO [merf.py:235] GLL is 976.9053972123901 at iteration 100.
---------------------Random Forest Fixed Effect Only----------------------
---------------------Random Forest w OHE Cluster----------------------
/Users/souravdey/.virtualenvs/merf/lib/python3.6/site-packages/pandas/core/internals.py:490: RuntimeWarning: None of the categories were found in values. Did you mean to use
'Categorical.from_codes(codes, categories)'?
return self.make_block(Categorical(self.values, **kwargs))
INFO [utils.py:164] Drew 10000 samples from 200 clusters.
INFO [utils.py:165] PTEV = 56.24401306543182, PREV = 15.559340292330981.
Experiment iteration: 0
---------------------MERF----------------------
INFO [merf.py:235] GLL is 595.3087050238545 at iteration 1.
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INFO [merf.py:235] GLL is 521.0550012436582 at iteration 100.
---------------------Random Forest Fixed Effect Only----------------------
---------------------Random Forest w OHE Cluster----------------------
/Users/souravdey/.virtualenvs/merf/lib/python3.6/site-packages/pandas/core/internals.py:490: RuntimeWarning: None of the categories were found in values. Did you mean to use
'Categorical.from_codes(codes, categories)'?
return self.make_block(Categorical(self.values, **kwargs))
INFO [utils.py:164] Drew 10000 samples from 200 clusters.
INFO [utils.py:165] PTEV = 57.308949748858936, PREV = 14.898563117357094.
Experiment iteration: 1
---------------------MERF----------------------
INFO [merf.py:235] GLL is 537.5808189039861 at iteration 1.
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INFO [merf.py:235] GLL is 372.4764806788835 at iteration 100.
---------------------Random Forest Fixed Effect Only----------------------
---------------------Random Forest w OHE Cluster----------------------
/Users/souravdey/.virtualenvs/merf/lib/python3.6/site-packages/pandas/core/internals.py:490: RuntimeWarning: None of the categories were found in values. Did you mean to use
'Categorical.from_codes(codes, categories)'?
return self.make_block(Categorical(self.values, **kwargs))
INFO [utils.py:164] Drew 10000 samples from 200 clusters.
INFO [utils.py:165] PTEV = 56.59725278831087, PREV = 15.337404228444518.
Experiment iteration: 2
---------------------MERF----------------------
INFO [merf.py:235] GLL is 515.4831878635628 at iteration 1.
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---------------------Random Forest Fixed Effect Only----------------------
---------------------Random Forest w OHE Cluster----------------------
/Users/souravdey/.virtualenvs/merf/lib/python3.6/site-packages/pandas/core/internals.py:490: RuntimeWarning: None of the categories were found in values. Did you mean to use
'Categorical.from_codes(codes, categories)'?
return self.make_block(Categorical(self.values, **kwargs))
INFO [utils.py:164] Drew 10000 samples from 200 clusters.
INFO [utils.py:165] PTEV = 56.81183916278223, PREV = 15.20392984056414.
Experiment iteration: 3
---------------------MERF----------------------
INFO [merf.py:235] GLL is 443.80421590298397 at iteration 1.
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INFO [merf.py:235] GLL is 373.80020865790914 at iteration 98.
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INFO [merf.py:235] GLL is 379.39381985578876 at iteration 100.
---------------------Random Forest Fixed Effect Only----------------------
---------------------Random Forest w OHE Cluster----------------------
/Users/souravdey/.virtualenvs/merf/lib/python3.6/site-packages/pandas/core/internals.py:490: RuntimeWarning: None of the categories were found in values. Did you mean to use
'Categorical.from_codes(codes, categories)'?
return self.make_block(Categorical(self.values, **kwargs))
INFO [utils.py:164] Drew 10000 samples from 200 clusters.
INFO [utils.py:165] PTEV = 56.77362896038165, PREV = 15.227623046532049.
Experiment iteration: 4
---------------------MERF----------------------
INFO [merf.py:235] GLL is 575.2928672947381 at iteration 1.
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INFO [merf.py:235] GLL is 453.9969433466266 at iteration 99.
INFO [merf.py:235] GLL is 455.66187943120167 at iteration 100.
---------------------Random Forest Fixed Effect Only----------------------
---------------------Random Forest w OHE Cluster----------------------
/Users/souravdey/.virtualenvs/merf/lib/python3.6/site-packages/pandas/core/internals.py:490: RuntimeWarning: None of the categories were found in values. Did you mean to use
'Categorical.from_codes(codes, categories)'?
return self.make_block(Categorical(self.values, **kwargs))
INFO [utils.py:164] Drew 10000 samples from 200 clusters.
INFO [utils.py:165] PTEV = 56.62620223475141, PREV = 15.319338417023545.
Experiment iteration: 5
---------------------MERF----------------------
INFO [merf.py:235] GLL is 570.0182955656078 at iteration 1.
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---------------------Random Forest Fixed Effect Only----------------------
---------------------Random Forest w OHE Cluster----------------------
/Users/souravdey/.virtualenvs/merf/lib/python3.6/site-packages/pandas/core/internals.py:490: RuntimeWarning: None of the categories were found in values. Did you mean to use
'Categorical.from_codes(codes, categories)'?
return self.make_block(Categorical(self.values, **kwargs))
INFO [utils.py:164] Drew 10000 samples from 200 clusters.
INFO [utils.py:165] PTEV = 56.3658008612481, PREV = 15.482508355079805.
Experiment iteration: 6
---------------------MERF----------------------
INFO [merf.py:235] GLL is 529.4990900434213 at iteration 1.
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INFO [merf.py:235] GLL is 414.14645670323955 at iteration 100.
---------------------Random Forest Fixed Effect Only----------------------
---------------------Random Forest w OHE Cluster----------------------
/Users/souravdey/.virtualenvs/merf/lib/python3.6/site-packages/pandas/core/internals.py:490: RuntimeWarning: None of the categories were found in values. Did you mean to use
'Categorical.from_codes(codes, categories)'?
return self.make_block(Categorical(self.values, **kwargs))
INFO [utils.py:164] Drew 10000 samples from 200 clusters.
INFO [utils.py:165] PTEV = 56.743365684030735, PREV = 15.246411204030139.
Experiment iteration: 7
---------------------MERF----------------------
INFO [merf.py:235] GLL is 562.9865098685796 at iteration 1.
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INFO [merf.py:235] GLL is 455.4885916841296 at iteration 97.
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INFO [merf.py:235] GLL is 460.08353318406284 at iteration 99.
INFO [merf.py:235] GLL is 461.85029055721566 at iteration 100.
---------------------Random Forest Fixed Effect Only----------------------
---------------------Random Forest w OHE Cluster----------------------
/Users/souravdey/.virtualenvs/merf/lib/python3.6/site-packages/pandas/core/internals.py:490: RuntimeWarning: None of the categories were found in values. Did you mean to use
'Categorical.from_codes(codes, categories)'?
return self.make_block(Categorical(self.values, **kwargs))
INFO [utils.py:164] Drew 10000 samples from 200 clusters.
INFO [utils.py:165] PTEV = 57.26988870800861, PREV = 14.922365751346753.
Experiment iteration: 8
---------------------MERF----------------------
INFO [merf.py:235] GLL is 586.7509768340144 at iteration 1.
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INFO [merf.py:235] GLL is 504.64929520484503 at iteration 100.
---------------------Random Forest Fixed Effect Only----------------------
---------------------Random Forest w OHE Cluster----------------------
/Users/souravdey/.virtualenvs/merf/lib/python3.6/site-packages/pandas/core/internals.py:490: RuntimeWarning: None of the categories were found in values. Did you mean to use
'Categorical.from_codes(codes, categories)'?
return self.make_block(Categorical(self.values, **kwargs))
INFO [utils.py:164] Drew 10000 samples from 200 clusters.
INFO [utils.py:165] PTEV = 56.93923715608889, PREV = 15.125163242306035.
Experiment iteration: 9
---------------------MERF----------------------
INFO [merf.py:235] GLL is 532.0701290188539 at iteration 1.
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INFO [merf.py:235] GLL is 404.34452268140774 at iteration 100.
---------------------Random Forest Fixed Effect Only----------------------
---------------------Random Forest w OHE Cluster----------------------
/Users/souravdey/.virtualenvs/merf/lib/python3.6/site-packages/pandas/core/internals.py:490: RuntimeWarning: None of the categories were found in values. Did you mean to use
'Categorical.from_codes(codes, categories)'?
return self.make_block(Categorical(self.values, **kwargs))
INFO [utils.py:164] Drew 10000 samples from 200 clusters.
INFO [utils.py:165] PTEV = 62.47897230868406, PREV = 30.02692450344678.
Experiment iteration: 0
---------------------MERF----------------------
INFO [merf.py:235] GLL is 595.967593805663 at iteration 1.
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INFO [merf.py:235] GLL is 517.418269681713 at iteration 100.
---------------------Random Forest Fixed Effect Only----------------------
---------------------Random Forest w OHE Cluster----------------------
/Users/souravdey/.virtualenvs/merf/lib/python3.6/site-packages/pandas/core/internals.py:490: RuntimeWarning: None of the categories were found in values. Did you mean to use
'Categorical.from_codes(codes, categories)'?
return self.make_block(Categorical(self.values, **kwargs))
INFO [utils.py:164] Drew 10000 samples from 200 clusters.
INFO [utils.py:165] PTEV = 61.63931719942464, PREV = 31.117056891192696.
Experiment iteration: 1
---------------------MERF----------------------
INFO [merf.py:235] GLL is 576.3777800407553 at iteration 1.
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INFO [merf.py:235] GLL is 518.6926905231687 at iteration 100.
---------------------Random Forest Fixed Effect Only----------------------
---------------------Random Forest w OHE Cluster----------------------
/Users/souravdey/.virtualenvs/merf/lib/python3.6/site-packages/pandas/core/internals.py:490: RuntimeWarning: None of the categories were found in values. Did you mean to use
'Categorical.from_codes(codes, categories)'?
return self.make_block(Categorical(self.values, **kwargs))
INFO [utils.py:164] Drew 10000 samples from 200 clusters.
INFO [utils.py:165] PTEV = 62.256864146292216, PREV = 30.312429296967448.
Experiment iteration: 2
---------------------MERF----------------------
INFO [merf.py:235] GLL is 622.0015207344643 at iteration 1.
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INFO [merf.py:235] GLL is 563.7343156114229 at iteration 100.
---------------------Random Forest Fixed Effect Only----------------------
---------------------Random Forest w OHE Cluster----------------------
/Users/souravdey/.virtualenvs/merf/lib/python3.6/site-packages/pandas/core/internals.py:490: RuntimeWarning: None of the categories were found in values. Did you mean to use
'Categorical.from_codes(codes, categories)'?
return self.make_block(Categorical(self.values, **kwargs))
INFO [utils.py:164] Drew 10000 samples from 200 clusters.
INFO [utils.py:165] PTEV = 62.2546233271569, PREV = 30.315320096377253.
Experiment iteration: 3
---------------------MERF----------------------
INFO [merf.py:235] GLL is 611.6154656878929 at iteration 1.
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INFO [merf.py:235] GLL is 546.9664169089664 at iteration 100.
---------------------Random Forest Fixed Effect Only----------------------
---------------------Random Forest w OHE Cluster----------------------
/Users/souravdey/.virtualenvs/merf/lib/python3.6/site-packages/pandas/core/internals.py:490: RuntimeWarning: None of the categories were found in values. Did you mean to use
'Categorical.from_codes(codes, categories)'?
return self.make_block(Categorical(self.values, **kwargs))
INFO [utils.py:164] Drew 10000 samples from 200 clusters.
INFO [utils.py:165] PTEV = 62.27692435817055, PREV = 30.28655961305538.
Experiment iteration: 4
---------------------MERF----------------------
INFO [merf.py:235] GLL is 676.646482985452 at iteration 1.
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INFO [merf.py:235] GLL is 609.7346408460684 at iteration 100.
---------------------Random Forest Fixed Effect Only----------------------
---------------------Random Forest w OHE Cluster----------------------
/Users/souravdey/.virtualenvs/merf/lib/python3.6/site-packages/pandas/core/internals.py:490: RuntimeWarning: None of the categories were found in values. Did you mean to use
'Categorical.from_codes(codes, categories)'?
return self.make_block(Categorical(self.values, **kwargs))
INFO [utils.py:164] Drew 10000 samples from 200 clusters.
INFO [utils.py:165] PTEV = 61.121836653306005, PREV = 31.803824521191913.
Experiment iteration: 5
---------------------MERF----------------------
INFO [merf.py:235] GLL is 593.4760057626453 at iteration 1.
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INFO [merf.py:235] GLL is 554.7378986214196 at iteration 100.
---------------------Random Forest Fixed Effect Only----------------------
---------------------Random Forest w OHE Cluster----------------------
/Users/souravdey/.virtualenvs/merf/lib/python3.6/site-packages/pandas/core/internals.py:490: RuntimeWarning: None of the categories were found in values. Did you mean to use
'Categorical.from_codes(codes, categories)'?
return self.make_block(Categorical(self.values, **kwargs))
INFO [utils.py:164] Drew 10000 samples from 200 clusters.
INFO [utils.py:165] PTEV = 61.686424280740624, PREV = 31.055111530610645.
Experiment iteration: 6
---------------------MERF----------------------
INFO [merf.py:235] GLL is 650.6004262665349 at iteration 1.
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INFO [merf.py:235] GLL is 569.9928339452802 at iteration 100.
---------------------Random Forest Fixed Effect Only----------------------
---------------------Random Forest w OHE Cluster----------------------
/Users/souravdey/.virtualenvs/merf/lib/python3.6/site-packages/pandas/core/internals.py:490: RuntimeWarning: None of the categories were found in values. Did you mean to use
'Categorical.from_codes(codes, categories)'?
return self.make_block(Categorical(self.values, **kwargs))
INFO [utils.py:164] Drew 10000 samples from 200 clusters.
INFO [utils.py:165] PTEV = 62.00919141128518, PREV = 30.633207532682643.
Experiment iteration: 7
---------------------MERF----------------------
INFO [merf.py:235] GLL is 616.221592851822 at iteration 1.
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INFO [merf.py:235] GLL is 552.3122215760183 at iteration 100.
---------------------Random Forest Fixed Effect Only----------------------
---------------------Random Forest w OHE Cluster----------------------
/Users/souravdey/.virtualenvs/merf/lib/python3.6/site-packages/pandas/core/internals.py:490: RuntimeWarning: None of the categories were found in values. Did you mean to use
'Categorical.from_codes(codes, categories)'?
return self.make_block(Categorical(self.values, **kwargs))
INFO [utils.py:164] Drew 10000 samples from 200 clusters.
INFO [utils.py:165] PTEV = 61.03106358081584, PREV = 31.92549345595334.
Experiment iteration: 8
---------------------MERF----------------------
INFO [merf.py:235] GLL is 587.6433789921442 at iteration 1.
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INFO [merf.py:235] GLL is 510.0045025494349 at iteration 100.
---------------------Random Forest Fixed Effect Only----------------------
---------------------Random Forest w OHE Cluster----------------------
/Users/souravdey/.virtualenvs/merf/lib/python3.6/site-packages/pandas/core/internals.py:490: RuntimeWarning: None of the categories were found in values. Did you mean to use
'Categorical.from_codes(codes, categories)'?
return self.make_block(Categorical(self.values, **kwargs))
INFO [utils.py:164] Drew 10000 samples from 200 clusters.
INFO [utils.py:165] PTEV = 61.526825297314666, PREV = 31.265366380251457.
Experiment iteration: 9
---------------------MERF----------------------
INFO [merf.py:235] GLL is 618.048180956219 at iteration 1.
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INFO [merf.py:235] GLL is 562.3035485404475 at iteration 100.
---------------------Random Forest Fixed Effect Only----------------------
---------------------Random Forest w OHE Cluster----------------------
/Users/souravdey/.virtualenvs/merf/lib/python3.6/site-packages/pandas/core/internals.py:490: RuntimeWarning: None of the categories were found in values. Did you mean to use
'Categorical.from_codes(codes, categories)'?
return self.make_block(Categorical(self.values, **kwargs))
INFO [utils.py:164] Drew 10000 samples from 200 clusters.
INFO [utils.py:165] PTEV = 56.71238634099818, PREV = 61.06265872675311.
Experiment iteration: 0
---------------------MERF----------------------
INFO [merf.py:235] GLL is 628.0959516194217 at iteration 1.
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INFO [merf.py:235] GLL is 542.9871955426755 at iteration 100.
---------------------Random Forest Fixed Effect Only----------------------
---------------------Random Forest w OHE Cluster----------------------
/Users/souravdey/.virtualenvs/merf/lib/python3.6/site-packages/pandas/core/internals.py:490: RuntimeWarning: None of the categories were found in values. Did you mean to use
'Categorical.from_codes(codes, categories)'?
return self.make_block(Categorical(self.values, **kwargs))
INFO [utils.py:164] Drew 10000 samples from 200 clusters.
INFO [utils.py:165] PTEV = 56.750249399131356, PREV = 60.96854348136929.
Experiment iteration: 1
---------------------MERF----------------------
INFO [merf.py:235] GLL is 593.3204841080712 at iteration 1.
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INFO [merf.py:235] GLL is 512.8935437111896 at iteration 100.
---------------------Random Forest Fixed Effect Only----------------------
---------------------Random Forest w OHE Cluster----------------------
/Users/souravdey/.virtualenvs/merf/lib/python3.6/site-packages/pandas/core/internals.py:490: RuntimeWarning: None of the categories were found in values. Did you mean to use
'Categorical.from_codes(codes, categories)'?
return self.make_block(Categorical(self.values, **kwargs))
INFO [utils.py:164] Drew 10000 samples from 200 clusters.
INFO [utils.py:165] PTEV = 56.34185124603779, PREV = 61.9903645882171.
Experiment iteration: 2
---------------------MERF----------------------
INFO [merf.py:235] GLL is 578.9010029256082 at iteration 1.
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INFO [merf.py:235] GLL is 514.4827175065012 at iteration 100.
---------------------Random Forest Fixed Effect Only----------------------
---------------------Random Forest w OHE Cluster----------------------
/Users/souravdey/.virtualenvs/merf/lib/python3.6/site-packages/pandas/core/internals.py:490: RuntimeWarning: None of the categories were found in values. Did you mean to use
'Categorical.from_codes(codes, categories)'?
return self.make_block(Categorical(self.values, **kwargs))
INFO [utils.py:164] Drew 10000 samples from 200 clusters.
INFO [utils.py:165] PTEV = 57.046662003250894, PREV = 60.23607550506824.
Experiment iteration: 3
---------------------MERF----------------------
INFO [merf.py:235] GLL is 597.6821591842138 at iteration 1.
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INFO [merf.py:235] GLL is 527.5697338031295 at iteration 100.
---------------------Random Forest Fixed Effect Only----------------------
---------------------Random Forest w OHE Cluster----------------------
/Users/souravdey/.virtualenvs/merf/lib/python3.6/site-packages/pandas/core/internals.py:490: RuntimeWarning: None of the categories were found in values. Did you mean to use
'Categorical.from_codes(codes, categories)'?
return self.make_block(Categorical(self.values, **kwargs))
INFO [utils.py:164] Drew 10000 samples from 200 clusters.
INFO [utils.py:165] PTEV = 56.346959574928626, PREV = 61.97749195964729.
Experiment iteration: 4
---------------------MERF----------------------
INFO [merf.py:235] GLL is 616.117895090049 at iteration 1.
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INFO [merf.py:235] GLL is 525.2018217023743 at iteration 100.
---------------------Random Forest Fixed Effect Only----------------------
---------------------Random Forest w OHE Cluster----------------------
/Users/souravdey/.virtualenvs/merf/lib/python3.6/site-packages/pandas/core/internals.py:490: RuntimeWarning: None of the categories were found in values. Did you mean to use
'Categorical.from_codes(codes, categories)'?
return self.make_block(Categorical(self.values, **kwargs))
INFO [utils.py:164] Drew 10000 samples from 200 clusters.
INFO [utils.py:165] PTEV = 56.82302529187944, PREV = 60.78799850776825.
Experiment iteration: 5
---------------------MERF----------------------
INFO [merf.py:235] GLL is 606.6247866141372 at iteration 1.
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INFO [merf.py:235] GLL is 532.4353980603515 at iteration 100.
---------------------Random Forest Fixed Effect Only----------------------
---------------------Random Forest w OHE Cluster----------------------
/Users/souravdey/.virtualenvs/merf/lib/python3.6/site-packages/pandas/core/internals.py:490: RuntimeWarning: None of the categories were found in values. Did you mean to use
'Categorical.from_codes(codes, categories)'?
return self.make_block(Categorical(self.values, **kwargs))
INFO [utils.py:164] Drew 10000 samples from 200 clusters.
INFO [utils.py:165] PTEV = 56.56897084992981, PREV = 61.4202853579035.
Experiment iteration: 6
---------------------MERF----------------------
INFO [merf.py:235] GLL is 652.6065321974577 at iteration 1.
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INFO [merf.py:235] GLL is 571.8478760239822 at iteration 100.
---------------------Random Forest Fixed Effect Only----------------------
---------------------Random Forest w OHE Cluster----------------------
/Users/souravdey/.virtualenvs/merf/lib/python3.6/site-packages/pandas/core/internals.py:490: RuntimeWarning: None of the categories were found in values. Did you mean to use
'Categorical.from_codes(codes, categories)'?
return self.make_block(Categorical(self.values, **kwargs))
INFO [utils.py:164] Drew 10000 samples from 200 clusters.
INFO [utils.py:165] PTEV = 56.49234458159288, PREV = 61.61210796350398.
Experiment iteration: 7
---------------------MERF----------------------
INFO [merf.py:235] GLL is 668.0924198715602 at iteration 1.
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INFO [merf.py:235] GLL is 615.0654573491655 at iteration 62.
INFO [merf.py:235] GLL is 610.1281042152541 at iteration 63.
INFO [merf.py:235] GLL is 609.7889027109109 at iteration 64.
INFO [merf.py:235] GLL is 600.7001883739937 at iteration 65.
INFO [merf.py:235] GLL is 609.705309842418 at iteration 66.
INFO [merf.py:235] GLL is 610.2445356654986 at iteration 67.
INFO [merf.py:235] GLL is 607.5138492106122 at iteration 68.
INFO [merf.py:235] GLL is 600.6412104099845 at iteration 69.
INFO [merf.py:235] GLL is 606.4849138667423 at iteration 70.
INFO [merf.py:235] GLL is 603.0228853516574 at iteration 71.
INFO [merf.py:235] GLL is 605.1483036212537 at iteration 72.
INFO [merf.py:235] GLL is 603.7880863510761 at iteration 73.
INFO [merf.py:235] GLL is 605.9471467744532 at iteration 74.
INFO [merf.py:235] GLL is 602.706711974296 at iteration 75.
INFO [merf.py:235] GLL is 609.3927592483016 at iteration 76.
INFO [merf.py:235] GLL is 612.3623859754556 at iteration 77.
INFO [merf.py:235] GLL is 608.9849723294982 at iteration 78.
INFO [merf.py:235] GLL is 607.3032073589292 at iteration 79.
INFO [merf.py:235] GLL is 603.8449467159863 at iteration 80.
INFO [merf.py:235] GLL is 603.1030461531495 at iteration 81.
INFO [merf.py:235] GLL is 602.7965642712331 at iteration 82.
INFO [merf.py:235] GLL is 607.6754995807596 at iteration 83.
INFO [merf.py:235] GLL is 599.3353045105574 at iteration 84.
INFO [merf.py:235] GLL is 602.5334526176727 at iteration 85.
INFO [merf.py:235] GLL is 595.8625880771631 at iteration 86.
INFO [merf.py:235] GLL is 614.6269388961997 at iteration 87.
INFO [merf.py:235] GLL is 607.1369732279564 at iteration 88.
INFO [merf.py:235] GLL is 600.0439192502915 at iteration 89.
INFO [merf.py:235] GLL is 602.4161755534423 at iteration 90.
INFO [merf.py:235] GLL is 603.4787672561487 at iteration 91.
INFO [merf.py:235] GLL is 603.0391756349951 at iteration 92.
INFO [merf.py:235] GLL is 601.3310127507954 at iteration 93.
INFO [merf.py:235] GLL is 604.1304505916277 at iteration 94.
INFO [merf.py:235] GLL is 607.49218194674 at iteration 95.
INFO [merf.py:235] GLL is 603.3093393011256 at iteration 96.
INFO [merf.py:235] GLL is 602.804480958077 at iteration 97.
INFO [merf.py:235] GLL is 605.7804546572249 at iteration 98.
INFO [merf.py:235] GLL is 608.9959694598648 at iteration 99.
INFO [merf.py:235] GLL is 605.8026988526157 at iteration 100.
---------------------Random Forest Fixed Effect Only----------------------
---------------------Random Forest w OHE Cluster----------------------
/Users/souravdey/.virtualenvs/merf/lib/python3.6/site-packages/pandas/core/internals.py:490: RuntimeWarning: None of the categories were found in values. Did you mean to use
'Categorical.from_codes(codes, categories)'?
return self.make_block(Categorical(self.values, **kwargs))
INFO [utils.py:164] Drew 10000 samples from 200 clusters.
INFO [utils.py:165] PTEV = 56.381114636122035, PREV = 61.89148355138391.
Experiment iteration: 8
---------------------MERF----------------------
INFO [merf.py:235] GLL is 679.4494714659709 at iteration 1.
INFO [merf.py:235] GLL is 618.8350361372537 at iteration 2.
INFO [merf.py:235] GLL is 617.1666004412117 at iteration 3.
INFO [merf.py:235] GLL is 610.5866813297092 at iteration 4.
INFO [merf.py:235] GLL is 620.9135580708028 at iteration 5.
INFO [merf.py:235] GLL is 617.8535485311471 at iteration 6.
INFO [merf.py:235] GLL is 614.0608613764073 at iteration 7.
INFO [merf.py:235] GLL is 614.2580544438064 at iteration 8.
INFO [merf.py:235] GLL is 619.3689242162775 at iteration 9.
INFO [merf.py:235] GLL is 616.7745606664986 at iteration 10.
INFO [merf.py:235] GLL is 612.8979791629472 at iteration 11.
INFO [merf.py:235] GLL is 617.8600343213483 at iteration 12.
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INFO [merf.py:235] GLL is 618.0392095798395 at iteration 21.
INFO [merf.py:235] GLL is 610.8761269855038 at iteration 22.
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INFO [merf.py:235] GLL is 623.6275943978026 at iteration 24.
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INFO [merf.py:235] GLL is 609.5603662057916 at iteration 26.
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INFO [merf.py:235] GLL is 610.8624283307785 at iteration 31.
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INFO [merf.py:235] GLL is 611.1704665410489 at iteration 33.
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INFO [merf.py:235] GLL is 613.5275922979814 at iteration 36.
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INFO [merf.py:235] GLL is 618.4768323638029 at iteration 39.
INFO [merf.py:235] GLL is 610.8958714583256 at iteration 40.
INFO [merf.py:235] GLL is 607.7507864025889 at iteration 41.
INFO [merf.py:235] GLL is 617.7950847198231 at iteration 42.
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INFO [merf.py:235] GLL is 616.4831429452437 at iteration 44.
INFO [merf.py:235] GLL is 615.4123111228055 at iteration 45.
INFO [merf.py:235] GLL is 608.2684850358895 at iteration 46.
INFO [merf.py:235] GLL is 607.5475870411193 at iteration 47.
INFO [merf.py:235] GLL is 610.4559630738137 at iteration 48.
INFO [merf.py:235] GLL is 610.1510147888547 at iteration 49.
INFO [merf.py:235] GLL is 614.7423560879494 at iteration 50.
INFO [merf.py:235] GLL is 614.5373985026168 at iteration 51.
INFO [merf.py:235] GLL is 611.1530582883624 at iteration 52.
INFO [merf.py:235] GLL is 618.1660056133969 at iteration 53.
INFO [merf.py:235] GLL is 614.8045478929836 at iteration 54.
INFO [merf.py:235] GLL is 615.6026795639486 at iteration 55.
INFO [merf.py:235] GLL is 616.8590671500997 at iteration 56.
INFO [merf.py:235] GLL is 624.097275268667 at iteration 57.
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INFO [merf.py:235] GLL is 619.1099629910248 at iteration 60.
INFO [merf.py:235] GLL is 616.003158690165 at iteration 61.
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INFO [merf.py:235] GLL is 609.0616238814381 at iteration 64.
INFO [merf.py:235] GLL is 617.4667004890451 at iteration 65.
INFO [merf.py:235] GLL is 617.7261014853016 at iteration 66.
INFO [merf.py:235] GLL is 612.2565560966694 at iteration 67.
INFO [merf.py:235] GLL is 616.1491686794637 at iteration 68.
INFO [merf.py:235] GLL is 618.1554982834359 at iteration 69.
INFO [merf.py:235] GLL is 620.7381298523223 at iteration 70.
INFO [merf.py:235] GLL is 615.041369016319 at iteration 71.
INFO [merf.py:235] GLL is 617.6804064274087 at iteration 72.
INFO [merf.py:235] GLL is 612.1691097738853 at iteration 73.
INFO [merf.py:235] GLL is 610.6977388543452 at iteration 74.
INFO [merf.py:235] GLL is 615.8576021486269 at iteration 75.
INFO [merf.py:235] GLL is 618.4007469207921 at iteration 76.
INFO [merf.py:235] GLL is 614.3911625272823 at iteration 77.
INFO [merf.py:235] GLL is 609.8029213868238 at iteration 78.
INFO [merf.py:235] GLL is 611.3180600522991 at iteration 79.
INFO [merf.py:235] GLL is 614.1603970698567 at iteration 80.
INFO [merf.py:235] GLL is 617.8170395505022 at iteration 81.
INFO [merf.py:235] GLL is 616.4107916863111 at iteration 82.
INFO [merf.py:235] GLL is 611.6683961825079 at iteration 83.
INFO [merf.py:235] GLL is 616.3523053440701 at iteration 84.
INFO [merf.py:235] GLL is 616.001127821985 at iteration 85.
INFO [merf.py:235] GLL is 615.8960873197569 at iteration 86.
INFO [merf.py:235] GLL is 615.7020578474021 at iteration 87.
INFO [merf.py:235] GLL is 616.5948517665524 at iteration 88.
INFO [merf.py:235] GLL is 616.0097163505241 at iteration 89.
INFO [merf.py:235] GLL is 607.8899859238802 at iteration 90.
INFO [merf.py:235] GLL is 610.7723879060978 at iteration 91.
INFO [merf.py:235] GLL is 619.2276033324521 at iteration 92.
INFO [merf.py:235] GLL is 619.6246648038439 at iteration 93.
INFO [merf.py:235] GLL is 614.6263670403664 at iteration 94.
INFO [merf.py:235] GLL is 610.3998685457625 at iteration 95.
INFO [merf.py:235] GLL is 620.9724659753414 at iteration 96.
INFO [merf.py:235] GLL is 613.8550693830412 at iteration 97.
INFO [merf.py:235] GLL is 618.8413682649747 at iteration 98.
INFO [merf.py:235] GLL is 611.7939349407595 at iteration 99.
INFO [merf.py:235] GLL is 609.967654651767 at iteration 100.
---------------------Random Forest Fixed Effect Only----------------------
---------------------Random Forest w OHE Cluster----------------------
/Users/souravdey/.virtualenvs/merf/lib/python3.6/site-packages/pandas/core/internals.py:490: RuntimeWarning: None of the categories were found in values. Did you mean to use
'Categorical.from_codes(codes, categories)'?
return self.make_block(Categorical(self.values, **kwargs))
INFO [utils.py:164] Drew 10000 samples from 200 clusters.
INFO [utils.py:165] PTEV = 56.30792291182036, PREV = 62.07592087046441.
Experiment iteration: 9
---------------------MERF----------------------
INFO [merf.py:235] GLL is 641.5700205087444 at iteration 1.
INFO [merf.py:235] GLL is 570.902090077464 at iteration 2.
INFO [merf.py:235] GLL is 572.7322784985305 at iteration 3.
INFO [merf.py:235] GLL is 581.3657385154479 at iteration 4.
INFO [merf.py:235] GLL is 569.428850887362 at iteration 5.
INFO [merf.py:235] GLL is 580.6937119846679 at iteration 6.
INFO [merf.py:235] GLL is 568.1740335432909 at iteration 7.
INFO [merf.py:235] GLL is 577.0808703624435 at iteration 8.
INFO [merf.py:235] GLL is 570.575738694973 at iteration 9.
INFO [merf.py:235] GLL is 575.2617956567957 at iteration 10.
INFO [merf.py:235] GLL is 570.0034455926436 at iteration 11.
INFO [merf.py:235] GLL is 575.2435072532634 at iteration 12.
INFO [merf.py:235] GLL is 570.0182447287015 at iteration 13.
INFO [merf.py:235] GLL is 569.6794130166306 at iteration 14.
INFO [merf.py:235] GLL is 580.8892262324188 at iteration 15.
INFO [merf.py:235] GLL is 569.7504936836829 at iteration 16.
INFO [merf.py:235] GLL is 575.6191419560938 at iteration 17.
INFO [merf.py:235] GLL is 571.4110213521288 at iteration 18.
INFO [merf.py:235] GLL is 570.4450549896354 at iteration 19.
INFO [merf.py:235] GLL is 573.2292450244425 at iteration 20.
INFO [merf.py:235] GLL is 581.3594212417905 at iteration 21.
INFO [merf.py:235] GLL is 573.8997337980613 at iteration 22.
INFO [merf.py:235] GLL is 570.0730204273387 at iteration 23.
INFO [merf.py:235] GLL is 572.6804004600074 at iteration 24.
INFO [merf.py:235] GLL is 577.9380445303279 at iteration 25.
INFO [merf.py:235] GLL is 577.3291789290871 at iteration 26.
INFO [merf.py:235] GLL is 580.9041624412475 at iteration 27.
INFO [merf.py:235] GLL is 571.5275193668041 at iteration 28.
INFO [merf.py:235] GLL is 571.8993297335463 at iteration 29.
INFO [merf.py:235] GLL is 571.4446552140117 at iteration 30.
INFO [merf.py:235] GLL is 580.294044292146 at iteration 31.
INFO [merf.py:235] GLL is 575.0252049146477 at iteration 32.
INFO [merf.py:235] GLL is 576.7326077930522 at iteration 33.
INFO [merf.py:235] GLL is 569.1421315722115 at iteration 34.
INFO [merf.py:235] GLL is 566.5949712034032 at iteration 35.
INFO [merf.py:235] GLL is 569.7756152162565 at iteration 36.
INFO [merf.py:235] GLL is 575.339705829042 at iteration 37.
INFO [merf.py:235] GLL is 580.9152491535341 at iteration 38.
INFO [merf.py:235] GLL is 563.5178978899349 at iteration 39.
INFO [merf.py:235] GLL is 574.6514270890309 at iteration 40.
INFO [merf.py:235] GLL is 574.9405027690098 at iteration 41.
INFO [merf.py:235] GLL is 573.3475717816755 at iteration 42.
INFO [merf.py:235] GLL is 577.7024605928926 at iteration 43.
INFO [merf.py:235] GLL is 575.1119631946988 at iteration 44.
INFO [merf.py:235] GLL is 568.7204849826535 at iteration 45.
INFO [merf.py:235] GLL is 573.0201383366632 at iteration 46.
INFO [merf.py:235] GLL is 575.1507915042042 at iteration 47.
INFO [merf.py:235] GLL is 572.6672286015427 at iteration 48.
INFO [merf.py:235] GLL is 573.0202720620246 at iteration 49.
INFO [merf.py:235] GLL is 577.3225266027996 at iteration 50.
INFO [merf.py:235] GLL is 579.5781841083692 at iteration 51.
INFO [merf.py:235] GLL is 572.1168916100438 at iteration 52.
INFO [merf.py:235] GLL is 569.8191422395992 at iteration 53.
INFO [merf.py:235] GLL is 574.9335723341924 at iteration 54.
INFO [merf.py:235] GLL is 575.7025870006552 at iteration 55.
INFO [merf.py:235] GLL is 574.2083879231142 at iteration 56.
INFO [merf.py:235] GLL is 572.6699804512152 at iteration 57.
INFO [merf.py:235] GLL is 570.7619105083519 at iteration 58.
INFO [merf.py:235] GLL is 576.1710558331399 at iteration 59.
INFO [merf.py:235] GLL is 576.5301348988477 at iteration 60.
INFO [merf.py:235] GLL is 570.1531614379106 at iteration 61.
INFO [merf.py:235] GLL is 574.0475671927965 at iteration 62.
INFO [merf.py:235] GLL is 570.7068476244472 at iteration 63.
INFO [merf.py:235] GLL is 574.792570968612 at iteration 64.
INFO [merf.py:235] GLL is 570.6626181414903 at iteration 65.
INFO [merf.py:235] GLL is 574.6904909555454 at iteration 66.
INFO [merf.py:235] GLL is 574.3368188117842 at iteration 67.
INFO [merf.py:235] GLL is 570.5006454029257 at iteration 68.
INFO [merf.py:235] GLL is 574.4030813988386 at iteration 69.
INFO [merf.py:235] GLL is 575.1568631387282 at iteration 70.
INFO [merf.py:235] GLL is 569.843635784478 at iteration 71.
INFO [merf.py:235] GLL is 574.6178637910226 at iteration 72.
INFO [merf.py:235] GLL is 568.0806228153346 at iteration 73.
INFO [merf.py:235] GLL is 574.5768059399892 at iteration 74.
INFO [merf.py:235] GLL is 577.5359695348748 at iteration 75.
INFO [merf.py:235] GLL is 579.1039160378435 at iteration 76.
INFO [merf.py:235] GLL is 575.5341639708874 at iteration 77.
INFO [merf.py:235] GLL is 568.2227882452707 at iteration 78.
INFO [merf.py:235] GLL is 574.2196446507481 at iteration 79.
INFO [merf.py:235] GLL is 568.974752547156 at iteration 80.
INFO [merf.py:235] GLL is 571.8266590464882 at iteration 81.
INFO [merf.py:235] GLL is 570.6698004000406 at iteration 82.
INFO [merf.py:235] GLL is 574.1657503445288 at iteration 83.
INFO [merf.py:235] GLL is 572.9157363500982 at iteration 84.
INFO [merf.py:235] GLL is 574.0160583543064 at iteration 85.
INFO [merf.py:235] GLL is 567.6562188614769 at iteration 86.
INFO [merf.py:235] GLL is 575.0065517546406 at iteration 87.
INFO [merf.py:235] GLL is 575.2308650064936 at iteration 88.
INFO [merf.py:235] GLL is 571.0569617406973 at iteration 89.
INFO [merf.py:235] GLL is 579.6783878210304 at iteration 90.
INFO [merf.py:235] GLL is 578.7666894828916 at iteration 91.
INFO [merf.py:235] GLL is 568.2331952720347 at iteration 92.
INFO [merf.py:235] GLL is 571.662916693766 at iteration 93.
INFO [merf.py:235] GLL is 576.3106641714355 at iteration 94.
INFO [merf.py:235] GLL is 579.3445108677704 at iteration 95.
INFO [merf.py:235] GLL is 567.624754635419 at iteration 96.
INFO [merf.py:235] GLL is 571.122827381849 at iteration 97.
INFO [merf.py:235] GLL is 573.2467544562319 at iteration 98.
INFO [merf.py:235] GLL is 573.206667647646 at iteration 99.
INFO [merf.py:235] GLL is 575.2418455705981 at iteration 100.
---------------------Random Forest Fixed Effect Only----------------------
---------------------Random Forest w OHE Cluster----------------------
/Users/souravdey/.virtualenvs/merf/lib/python3.6/site-packages/pandas/core/internals.py:490: RuntimeWarning: None of the categories were found in values. Did you mean to use
'Categorical.from_codes(codes, categories)'?
return self.make_block(Categorical(self.values, **kwargs))
In [1]:
import pickle
# pickle.dump(results, open("results_merf100_n10.pkl", "wb" ))
In [2]:
results = pickle.load(open("results_merf100_n10.pkl", "rb"))
In [3]:
def merf_gain(merf_mse, non_merf_mse):
return 100 * np.mean((np.array(non_merf_mse) - np.array(merf_mse)) / np.array(non_merf_mse))
In [31]:
summary_results = pd.DataFrame()
for experiment, result in zip(experiments, results):
summary_results.loc[result['id'], 'm'] = experiment['m']
summary_results.loc[result['id'], 'sigma_b2'] = experiment['sigma_b_sq']
summary_results.loc[result['id'], 'sigma_e2'] = experiment['sigma_e']
summary_results.loc[result['id'], 'PTEV'] = np.round(np.mean(np.array(result['ptev'])), 2)
summary_results.loc[result['id'], 'PREV'] = np.round(np.mean(np.array(result['prev'])), 2)
summary_results.loc[result['id'], 'Gain RF (Known)'] = np.round(merf_gain(result['mse_known_merf'], result['mse_known_rf_fixed']), 2)
summary_results.loc[result['id'], 'Gain RF (New)']= np.round(merf_gain(result['mse_new_merf'], result['mse_new_rf_fixed']), 2)
summary_results.loc[result['id'], 'Gain RFOHE (Known)'] = np.round(merf_gain(result['mse_known_merf'], result['mse_known_rf_ohe']), 2)
summary_results.loc[result['id'], 'Gain RFOHE (New)'] = np.round(merf_gain(result['mse_new_merf'], result['mse_new_rf_ohe']), 2)
In [36]:
summary_results
Out[36]:
m
sigma_b2
sigma_e2
PTEV
PREV
Gain RF (Known)
Gain RF (New)
Gain RFOHE (Known)
Gain RFOHE (New)
0
0.8
0.9
1.0
89.91
10.10
17.69
1.29
18.27
4.26
1
0.7
2.7
1.0
89.73
30.90
50.02
3.40
41.44
2.68
2
0.6
4.5
1.0
89.94
50.36
68.67
-0.39
56.02
-3.84
3
0.3
0.2
1.0
56.77
15.23
5.53
0.44
2.16
-1.23
4
0.3
0.5
1.0
61.83
30.87
19.18
2.46
10.97
0.30
5
0.2
0.8
1.0
56.58
61.40
33.79
4.72
15.79
0.32
In [33]:
plt.figure(figsize=[16, 8])
plt.subplot(121)
plt.plot(summary_results.loc[0:2, 'PREV'],
summary_results.loc[0:2, 'Gain RF (Known)'], 'bs-', label='RF, PTEV=90')
plt.plot(summary_results.loc[3:5, 'PREV'],
summary_results.loc[3:5, 'Gain RF (Known)'], 'rs-', label='RF, PTEV=60')
plt.plot(summary_results.loc[0:2, 'PREV'],
summary_results.loc[0:2, 'Gain RFOHE (Known)'], 'b^--', label='RFOHE, PTEV=90')
plt.plot(summary_results.loc[3:5, 'PREV'],
summary_results.loc[3:5, 'Gain RFOHE (Known)'], 'r^--', label='RFOHE, PTEV=60')
plt.grid('on')
plt.xlabel('PREV')
plt.ylabel('MERF Gain over Compared Algorithm')
#plt.legend()
plt.title('Known Clusters')
plt.ylim([-5, 75])
plt.xlim([0, 65])
plt.subplot(122)
plt.plot(summary_results.loc[0:2, 'PREV'],
summary_results.loc[0:2, 'Gain RF (New)'], 'bs-', label='RF, PTEV=90')
plt.plot(summary_results.loc[3:5, 'PREV'],
summary_results.loc[3:5, 'Gain RF (New)'], 'rs-', label='RF, PTEV=60')
plt.plot(summary_results.loc[0:2, 'PREV'],
summary_results.loc[0:2, 'Gain RFOHE (New)'], 'b^--', label='RFOHE, PTEV=90')
plt.plot(summary_results.loc[3:5, 'PREV'],
summary_results.loc[3:5, 'Gain RFOHE (New)'], 'r^--', label='RFOHE, PTEV=60')
plt.grid('on')
plt.xlabel('PREV')
#plt.ylabel('MERF %-gain over Compared Algorithm')
plt.legend()
plt.title('New Clusters')
plt.ylim([-5, 75])
plt.xlim([0, 65])
Out[33]:
(0, 65)
In [ ]:
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