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MIAMI_test_on_adult_old.py
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MIAMI_test_on_adult_old.py
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# -*- coding: utf-8 -*-
"""
Created on Mon April 29 13:25:11 2020
@author: rfuchs
"""
import os
os.chdir('C:/Users/rfuchs/Documents/GitHub/M1DGMM')
import pandas as pd
from copy import deepcopy
from gower import gower_matrix
import matplotlib.pyplot as plt
from sklearn.preprocessing import LabelEncoder
from miami import MIAMI
from init_params import dim_reduce_init
from data_preprocessing import compute_nj
import autograd.numpy as np
from table_evaluator import TableEvaluator
import seaborn as sns
###############################################################################
###################### Adult data ############################
###############################################################################
#===========================================#
# Model Hyper-parameters
#===========================================#
n_clusters = 'auto'
r = np.array([3, 1])
k = [4]
seed = 1
init_seed = 2
# !!! Changed eps
eps = 1E-02
it = 10
maxstep = 100
var_distrib = np.array(['continuous', 'categorical', 'continuous', 'ordinal',\
'binomial', 'categorical', 'categorical', 'categorical',\
'categorical', 'bernoulli', 'ordinal', 'ordinal',\
'continuous', 'categorical', 'bernoulli'])
# Plotting utilities
varnames = ['age', 'workclass', 'fnlwgt', 'education',
'education.num', 'marital.status', 'occupation', 'relationship',\
'race', 'sex', 'capital.gain', 'capital.loss',\
'hours.per.week', 'native.country', 'income']
p_new = len(var_distrib)
#===========================================#
# Importing data
#===========================================#
os.chdir('C:/Users/rfuchs/Documents/These/Stats/mixed_dgmm/datasets')
experiment_designs = ['Absent', 'Small', 'Unbalanced']
nb_files_per_design = 10
inf_nb = 1E12
'''
design = experiment_designs[0]
filenum = 1
'''
acceptance_rate = dict(zip(experiment_designs, [[],[],[]]))
for design in experiment_designs:
#*****************************************************************
# File name formatting and sampling rules
#*****************************************************************
if design in ['Absent', 'Unbalanced']:
prefix = design[:3] + '_'
# Want to sample only women of more than 60 years old
authorized_ranges = np.expand_dims(np.stack([[-inf_nb,inf_nb] for var in var_distrib]).T, 1)
authorized_ranges[:,0, 0] = [60, 100] # Of more than 60 years old
authorized_ranges[:,0, 9] = [0, 0] # Only women
nb_pobs = 200
elif design == 'Small':
prefix = '' #
authorized_ranges = np.expand_dims(np.stack([[-inf_nb,inf_nb] for var in var_distrib]).T, 1)
nb_pobs = 5000
#*****************************************************************
# Generate data for all experiment design
#*****************************************************************
for filenum in range(1, nb_files_per_design + 1):
if design in ['Absent', 'Unbalanced']:
train_filepath = 'adult/' + design + '/' + prefix + 'Train_' + str(filenum) + '.csv'
elif design == 'Small':
train_filepath = 'adult/' + design + '/' + prefix + 'Train_' + str(filenum) + '.csv'
else:
raise RuntimeError('Please specify of valid design')
train = pd.read_csv(train_filepath, sep = ',')
train = train.infer_objects()
p = train.shape[1]
# Import the test set
test_filepath = 'adult/' + design + '/' + prefix + 'Test_' + str(filenum) + '.csv'
test = pd.read_csv(test_filepath, sep = ',')
# Delete the missing values
train = train.loc[~(train == '?').any(1)]
test = test.loc[~(test == '?').any(1)]
numobs = len(train)
#*****************************************************************
# Formating the data
#*****************************************************************
# Encode categorical datas
categ_dict = {} # Store the Label encoding
for col_idx, colname in enumerate(train.columns):
if var_distrib[col_idx] == 'categorical':
le = LabelEncoder()
# Keep only the modalities that the two datasets have in common
test = test[test[colname].isin(list(set(train[colname])))]
# Convert them into numerical values
train[colname] = le.fit_transform(train[colname])
categ_dict[colname] = deepcopy(le)
# Encode binary data
bin_dict = {} # Store the observations
for col_idx, colname in enumerate(train.columns):
le = LabelEncoder()
if var_distrib[col_idx] == 'bernoulli':
train[colname] = le.fit_transform(train[colname])
bin_dict[colname] = deepcopy(le)
# Encode ordinal data, modalities have been sorted (at best)
ord_modalities = ['Preschool', '1st-4th', '5th-6th', '7th-8th', '9th',\
'10th', '11th', '12th', 'HS-grad', 'Some-college','Masters',\
'Bachelors', 'Prof-school', 'Assoc-acdm', 'Assoc-voc', 'Doctorate']
# Delete non-existing modalities in the test set
test = test[test['education'].isin(ord_modalities)]
for idx, mod in enumerate(ord_modalities):
train['education'] = np.where(train['education'] == mod, idx, train['education'])
train['education'] = train['education'].astype(int)
ord_le = LabelEncoder()
train['education.num'] = ord_le.fit_transform(train['education.num'])
# Encode capital.gain and capital.loss and capital.gain as ordinal variables
k_dict = {}
nb_bins = 5 # The size of each class
for col in ['capital.gain', 'capital.loss']:
le = LabelEncoder()
#step = np.ceil(test[col].max()) / nb_bins
# Create the intervals for each class
#bins = pd.IntervalIndex.from_tuples([(-0.5, 0.5)] +\
#[(1 + i*step, 1 + (i + 1) * step) for i in range(nb_bins)])
#discrete_k = pd.cut(train[col], bins).map(lambda x: x.mid).astype(float)
#print(set(discrete_k))
#test[col] = pd.cut(test[col], bins).map(lambda x: x.mid).astype(float)
le.fit(test[col].append(train[col]))
train[col] = le.transform(train[col])
k_dict[col] = deepcopy(le)
nj, nj_bin, nj_ord, nj_categ = compute_nj(train, var_distrib)
nb_cont = np.sum(var_distrib == 'continuous')
p_new = train.shape[1]
train_np = train.values
# Defining distances over the features
cat_features = pd.Series(var_distrib).isin(['categorical', 'bernoulli']).to_list()
dm = gower_matrix(train.astype(np.object), cat_features = cat_features)
dtype = {train.columns[j]: np.float64 if (var_distrib[j] != 'bernoulli') and \
(var_distrib[j] != 'categorical') else np.str for j in range(p_new)}
train = train.astype(dtype, copy=True)
numobs = len(train)
#*****************************************************************
# Run MIAMI
#*****************************************************************
prince_init = dim_reduce_init(train, 2, k, r, nj, var_distrib, seed = None,\
use_famd=True)
out = MIAMI(train_np, 'auto', r, k, prince_init, var_distrib, nj, authorized_ranges, nb_pobs, it,\
eps, maxstep, seed, perform_selec = False, dm = dm, max_patience = 0)
print('MIAMI has kept one observation over', round(1 / out['share_kept_pseudo_obs']),\
'observations generated')
acceptance_rate[design].append(out['share_kept_pseudo_obs'])
#*****************************************************************
# Visualisation result
#*****************************************************************
train_new_np = out['y_all'][len(train):]
train_new = pd.DataFrame(train_new_np, columns = train.columns)
le_dict = {**categ_dict, **bin_dict, **k_dict}
le_dict['education.num'] = ord_le
# Relabel the data
for col_idx, colname in enumerate(train.columns):
if (var_distrib[col_idx] != 'continuous') & (colname != 'education'):
le = le_dict[colname]
train_new[colname] = le.inverse_transform(train_new[colname].astype(int))
for idx, mod in enumerate(ord_modalities):
train_new['education'] = np.where(train_new['education'] == str(float(idx)), mod, train_new['education'])
# Keep only the women that have more than 60 years in the test
if design in ['Absent', 'Unb']:
test = test[(test['age'] >= 60) & (test['sex'] == 'Female')]
# Store the predictions
train_new.to_csv('pseudo_adult/' + design + '/' + 'preds' + str(filenum) + '.csv',\
index = False)
acceptance_rate = pd.DataFrame(acceptance_rate)
acceptance_rate.to_csv('pseudo_adult/acceptance_rate.csv')
acceptance_rate[['Unbalanced', 'Absent']].astype(float).boxplot()
plt.title('Acceptance rate of MIAMI in the Absent and Unbalanced designs')
plt.ylabel('Acceptance rate')
plt.xlabel('Design')
# Visualise the predictions
# Use table_evaluator
plt.plot([0])
plt.title('Design:' + design)
plt.show()
cat_features = (~(pd.Series(var_distrib) != 'continuous')).to_list()
table_evaluator = TableEvaluator(test, train_new)
table_evaluator.visual_evaluation()