/
extract_variables.py
64 lines (42 loc) · 2.25 KB
/
extract_variables.py
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import pandas as pd
import numpy as np
from functools import partial
from project.helper_functions import set_random_seed
from project.models.autoencoders import SymmetricAutoencoder
from project.models.trained_autoencoder import TrainedAutoencoder
from sklearn.preprocessing import StandardScaler
from keras.optimizers import RMSprop
from keras import metrics
from preprocess import impute_random, balance_x_y
def recode_variables(row, recodings):
recoded_dict = {name: func(row[name]) for name, func in recodings.items()}
return pd.Series(recoded_dict)
def get_from_file_autoencoder(db_path, model_path):
#db_path = './results/save/unsupervised/linear.txt'
#model_path = './results/save/unsupervised/model/linearautoencoder_0.h5'
df = pd.read_csv(db_path)
trained_autoencoder = TrainedAutoencoder.from_database(df, model_path, model_id=0)
return trained_autoencoder
def prepare_x_y(volumes, y, recodings):
set_random_seed()
volumes_ = volumes.iloc[:,1:] # 0th column is eid, not needed here
y_selected = (y
.iloc[:, 1:] # Oth column is eid, not needed here
.applymap(lambda x: np.nan if x < 0 else x) # below zero codes for missing reasons
.apply(impute_random)
.apply(partial(recode_variables,recodings=recodings), axis=1)
)
return volumes_, y_selected
def get_data_for_var(var,X_train_original, y_train_original, encoded, db_path, model_path, age=False):
set_random_seed()
train_nan = y_train_original[var].isnull()
X_train_bal, y_train_bal, y_train_ind = balance_x_y(X_train_original.loc[train_nan==False, :], y_train_original.loc[train_nan==False, var])
if encoded:
trained_autoencoder = get_from_file_autoencoder(db_path, model_path)
X_train_bal = trained_autoencoder.encoder.predict(X_train_bal)
if age:
X_train_bal = pd.DataFrame(X_train_bal)
X_train_bal[15] = StandardScaler().fit_transform(y_train_original.loc[y_train_ind, ['Age at recruitment']])
#X_train_bal[16] = y_train_original.loc[y_train_ind, ['Sites']].reset_index(drop=True)
X_train_bal = X_train_bal.values
return X_train_bal, y_train_bal