main.py

import numpy as np
import pandas as pd
from copy import deepcopy
import gc

from col_headers import Header
from experiments import Experiments, Configs
from experiments.useful import timestamped_to_vector, unison_shuffled_copies, extract_neg

# Load data
headers = Header()
c = headers.classification_col
v = headers.vector_col
data = pd.read_csv("data.csv")

test = data[data["test_set"] == True]
train = data[data["test_set"] == False]

train = train.as_matrix(columns=headers.training_headers)
test = test.as_matrix(columns=headers.training_headers)

x_test, y_test = timestamped_to_vector(test, vector_col=v, time_start=0, classification_col=c)
x_train, y_train = timestamped_to_vector(train, vector_col=v, time_start=0, classification_col=c)

# Random search with thresholding 
rand_params = Configs.get_all()

expt = Experiments.Experiment(rand_params, search_algorithm="random",
   data=(x_train, y_train), folds=10, folder_name="random_search_reults", 
   thresholding=True, threshold=0.5)


# parameter configurations
A_B_C = Configs.get_A_B_C

# Ensemble model 
ensemble_config = Experiments.Ensemble_configurations(list(A_B_C.values()),
	x_test=x_test, y_test=y_test,
	x_train=x_train, y_train=y_train, 
    folder_name="test_train_results", 
    batch_size=64)
ensemble_config.run_experiments()