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Copy pathDdimensionalExperiment_quadraticOnlyGen.py
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DdimensionalExperiment_quadraticOnlyGen.py
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import sys
import pandas as pd
sys.path.append("../PaperNon_Gen_LinCFA/")
from NonLinCFA.NonLinCFA import NonLinCFA
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score
import matplotlib.pyplot as plt
from NonLinCFA.NonLinCFA_anyFunction import NonLinCFA_anyFunction
from LinCFA.LinCFA import LinCFA
from GenLinCFA.GenLinCFA import GenLinCFA
from GenLinCFA.GenLinCFA_anyFunction import GenLinCFA_anyFunction
import numpy as np
from mlxtend.feature_selection import SequentialFeatureSelector as SFS
import random
from sklearn import preprocessing
import pickle
import argparse
from joblib import Parallel, delayed
### compute correlation between two random variables
def compute_corr(x1,x2):
return pearsonr(x1,x2)[0]
### compute test R2 score
def compute_r2(x_train, y_train, x_val, y_val):
regr = LinearRegression().fit(x_train,y_train)
y_pred = regr.predict(x_val)
return r2_score(y_val, y_pred)
def compute_wrapper(x_trainVal, y_trainVal,n_features):
sfs = SFS(LinearRegression(),
k_features=n_features,
forward=True,
floating=False,
scoring = 'r2',
cv = 5,
n_jobs=5)
sfs.fit(x_trainVal, y_trainVal)
return pd.DataFrame(sfs.subsets_).transpose()
def squared_aggregation(x):
x_sq = x**2
return x_sq.sum(axis=1)
def mean_aggregation(x):
return x.mean(axis=1)
def run_NonLinCFA(x,eps,passed_fun):
output = NonLinCFA_anyFunction(x.iloc[:2000,:],'target', eps, 5 , neigh=0, customFunction=passed_fun).compute_clusters()
aggregate_x = pd.DataFrame()
for i in range(len(output)):
aggregate_x[i] = passed_fun(x[output[i]])
actual_score = compute_r2(aggregate_x.iloc[:2000,:], x.loc[:,'target'].iloc[:2000], aggregate_x.iloc[2000:,:], x.loc[:,'target'].iloc[2000:])
return [eps,len(output),actual_score]
def run_GenLinCFA(x,eps,passed_fun):
output = GenLinCFA_anyFunction(x.iloc[:2000,:],'target', eps1=eps, n_val=5 , neigh=0, eps2=1, customFunction=passed_fun).compute_clusters()
aggregate_x = pd.DataFrame()
for i in range(len(output)):
aggregate_x[i] = passed_fun(x[output[i]])
actual_score = compute_r2(aggregate_x.iloc[:2000,:], x.loc[:,'target'].iloc[:2000], aggregate_x.iloc[2000:,:], x.loc[:,'target'].iloc[2000:])
return [eps,len(output),actual_score]
def quadratic_experiment_noResampling(n_reps=10, n_variables=100, noise=10):
NonLinCFA_score = []
list_of_length = []
wrapper_score = []
LinCFA_score = []
list_of_length_lin = []
GenLinCFA_score = []
list_of_length_gen = []
NonLinCFA_score_sq = []
GenLinCFA_score_sq = []
wrapper_score_sq = []
x_all,y_all,coeffs = generate_dataset_squared(n_data=3000*10, noise=noise, p1=0.3, p2=0.7, n_variables=n_variables, coeffs=[0])
for trials in range(n_reps):
x = pd.DataFrame(x_all[3000*trials:3000*(trials+1)])
x_squared = x**2
x['target'] = y_all[3000*trials:3000*(trials+1)]
x_squared['target'] = y_all[3000*trials:3000*(trials+1)]
results = Parallel(n_jobs=10)(delayed(run_GenLinCFA)(x_squared,eps,mean_aggregation) for eps in [0.68,0.7,0.72,0.74,0.76])
print(results)
GenLinCFA_score.append(results)
results = Parallel(n_jobs=10)(delayed(run_GenLinCFA)(x,eps,squared_aggregation) for eps in [0.68,0.7,0.72,0.74,0.76])
print(results)
GenLinCFA_score_sq.append(results)
return GenLinCFA_score,GenLinCFA_score_sq
### generate a dataset of n samples and standardize the variables x1,x2,x3
def generate_dataset_squared(n_data=3000, noise=1, p1=0.5, p2=0.5, n_variables=10, coeffs=[0]):
x = np.zeros((n_data,n_variables))
rng = np.random.default_rng(seed=0)
random.seed(30)
x[:,0] = rng.uniform(0,1,n_data)
if coeffs[0]==0:
coeffs = rng.uniform(0,1,size=n_variables)
for i in range(n_variables-1):
j = random.randrange(i+1)
x[:,i+1] = p1*rng.uniform(0,1,n_data) + p2*x[:,j]
scaler = preprocessing.StandardScaler().fit(x)
x = scaler.transform(x)
delta = rng.normal(0, noise, size=(n_data,1))
y = np.dot(x**2,coeffs).reshape(n_data,1) + delta.reshape(n_data,1)
return x,y,coeffs
### main run
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--n_variables", default=100, type=int)
parser.add_argument("--noise", default=10, type=float)
parser.add_argument("--n_repetitions", default=500, type=int)
parser.add_argument("--p1", default=0.3, type=float)
parser.add_argument("--p2", default=0.7, type=float)
parser.add_argument("--results_file", default='res.pkl')
args = parser.parse_args()
print(args)
##################### experiment ########################
GenLinCFA_score,GenLinCFA_score_sq = quadratic_experiment_noResampling(n_reps=args.n_repetitions, n_variables=args.n_variables, noise=args.noise)
##################### save the results ########################
with open(args.results_file, 'wb') as f:
pickle.dump([GenLinCFA_score,GenLinCFA_score_sq], f)