SimpleML_optimizingcv.py

import pandas as pd
import numpy as np
from sklearn import linear_model
from sklearn.linear_model import Ridge
from sklearn import metrics
from sklearn.svm import SVR
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.optimizers import SGD
from sklearn.preprocessing import StandardScaler
import time
import matplotlib.pyplot as plt
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OneHotEncoder
import pdb
import math
import dataprep_1 as dp
from sklearn.model_selection import KFold
from sklearn.decomposition import PCA
from keras.regularizers import l1
from keras.regularizers import l1_l2

start = time.time()

data_start = pd.read_csv("2048data.csv")

# data = wd.deleter(data)


# Prepping Data
data_start = data_start.sample(frac=1).reset_index(drop=True)
XLabels = ['TotalT', 'Temp', 'LSR', 'CA', 'Size', 'IsoT', 'HeatT', 'Ramp', 'F_X', 'Ro', 'logRo', 'P','Acid','Acetyl','Wood','Yield']
X = data_start[XLabels]
# The data preparation function

XLabels = ['TotalT', 'Temp', 'LSR', 'CA', 'Size', 'IsoT', 'HeatT', 'Ramp', 'F_X', 'Ro', 'logRo', 'P','Acid','Acetyl','Wood','Yield']
X_raw = data_start[XLabels]


X,Y,data,XLabels=dp.prep(X_raw,True)

best_lr = 0.01
best_bs = 256
best_dr = 0
dropout=0.001
initializer='lecun_uniform'

def validate(X,Y,modelname,epoch):
    kfold = KFold(n_splits=4, shuffle=True, random_state=1)
    cvscores = []
    trainingscores =[]
    split=0
    for train, test in kfold.split(X,Y):
    	# Fit the model
        modelname.fit(X[train], Y[train], epochs=epoch, batch_size=best_bs, verbose=0)
        y_pred = modelname.predict(X[test])
        y_train = modelname.predict(X[train])
        y_train = y_train.flatten()
        y_pred = y_pred.flatten()
        try:
            training_error = metrics.mean_absolute_error(Y[train], y_train)
            error = metrics.mean_absolute_error(Y[test], y_pred)
            trainingscores.append(training_error)
        except:
            print("Input contains null values. Skipping Config.")
            continue
        cvscores.append(error)
        split=split+1
    print("Validation Score: %.2f%% (+/- %.2f%%)" % (np.mean(cvscores), np.std(cvscores)))
    print("Training Score: %.2f%% (+/- %.2f%%)" % (np.mean(trainingscores), np.std(trainingscores)))
    return

epochs = [1,10,50,100,500,1000,2500,5000,10000]


for epoch in epochs:
    try:
        model = Sequential()
        model.add(Dense(units=96, activation='softsign', input_dim=39, kernel_initializer=initializer))
        model.add(Dropout(dropout))
        model.add(Dense(units=48, activation='softsign', kernel_initializer=initializer))
        model.add(Dense(units=48, activation='softsign', kernel_initializer=initializer))
        model.add(Dense(units=1, activation='linear'))
    except:
        print("That isn't a valid initializer, stupid")
        continue
    sgd = SGD(lr=best_lr)
    model.compile(loss='mean_squared_error', optimizer=sgd, metrics=['accuracy'])
    print('Number of Epochs:',epoch)
    validate(X,Y,model,epoch)
    end1 = time.time()
    duration = end1 - start
    print("Execution Time of Neural Net is:", duration /60, "min\n")
    start = end1