我是使用Python进行机器学习的新手。我来自Fortran的编程背景，因此您可以想象，Python是一个相当飞跃。我从事化学工作，已经参与化学形象（将数据科学技术应用于化学）。因此，Python广泛的机器学习库的应用很重要。我还需要我的代码有效。我编写了一个运行的代码，似乎可以正常工作。我想知道的是：
1如何最好地改善它/使其更有效。
2关于我使用过的替代配方的任何建议，如果可能的话，另一个路线可能会出色的原因？
我倾向于与连续的数据和回归模型一起工作。
任何建议都很棒，并事先感谢您。
import scipy
import math
import numpy as np
import pandas as pd
import plotly.plotly as py
import os.path
import sys
from time import time
from sklearn import preprocessing, metrics, cross_validation
from sklearn.cross_validation import train_test_split
from sklearn.ensemble import RandomForestRegressor
from sklearn.grid_search import GridSearchCV
from sklearn.cross_validation import KFold
fname = str(raw_input('Please enter the input file name containing total dataset and descriptors (assumes csv file, column headings and first column are labelsn'))
if os.path.isfile(fname) :
SubFeAll = pd.read_csv(fname, sep=",")
else:
sys.exit("ERROR: input file does not exist")
#SubFeAll = pd.read_csv(fname, sep=",")
SubFeAll = SubFeAll.fillna(SubFeAll.mean()) # replace the NA values with the mean of the descriptor
header = SubFeAll.columns.values # Use the column headers as the descriptor labels
SubFeAll.head()
# Set the numpy global random number seed (similar effect to random_state)
np.random.seed(1)
# Random Forest results initialised
RFr2 = []
RFmse = []
RFrmse = []
# Predictions results initialised
RFpredictions = []
metcount = 0
# Give the array from pandas to numpy
npArray = np.array(SubFeAll)
print header.shape
npheader = np.array(header[1:-1])
print("Array shape X = %d, Y = %d " % (npArray.shape))
datax, datay = npArray.shape
# Print specific nparray values to check the data
print("The first element of the input data set, as a minial check please ensure this is as expected = %s" % npArray[0,0])
# Split the data into: names labels of the molecules ; y the True results ; X the descriptors for each data point
names = npArray[:,0]
X = npArray[:,1:-1].astype(float)
y = npArray[:,-1] .astype(float)
X = preprocessing.scale(X)
print X.shape
# Open output files
train_name = "Training.csv"
test_name = "Predictions.csv"
fi_name = "Feature_importance.csv"
with open(train_name,'w') as ftrain, open(test_name,'w') as fpred, open(fi_name,'w') as ffeatimp:
ftrain.write("This file contains the training information for the Random Forest modelsn")
ftrain.write("The code use a ten fold cross validation 90% training 10% test at each fold so ten training sets are used here,n")
ftrain.write("Interation %d ,n" %(metcount+1))
fpred.write("This file contains the prediction information for the Random Forest modelsn")
fpred.write("Predictions are made over a ten fold cross validation hence training on 90% test on 10%. The final prediction are return iteratively over this ten fold cros validation once,n")
fpred.write("optimised parameters are located via a grid search at each fold,n")
fpred.write("Interation %d ,n" %(metcount+1))
ffeatimp.write("This file contains the feature importance information for the Random Forest model,n")
ffeatimp.write("Interation %d ,n" %(metcount+1))
# Begin the K-fold cross validation over ten folds
kf = KFold(datax, n_folds=10, shuffle=True, random_state=0)
print "------------------- Begining Ten Fold Cross Validation -------------------"
for train, test in kf:
XTrain, XTest, yTrain, yTest = X[train], X[test], y[train], y[test]
ytestdim = yTest.shape[0]
print("The test set values are : ")
i = 0
if ytestdim%5 == 0:
while i < ytestdim:
print round(yTest[i],2),'t', round(yTest[i+1],2),'t', round(yTest[i+2],2),'t', round(yTest[i+3],2),'t', round(yTest[i+4],2)
ftrain.write(str(round(yTest[i],2))+','+ str(round(yTest[i+1],2))+','+str(round(yTest[i+2],2))+','+str(round(yTest[i+3],2))+','+str(round(yTest[i+4],2))+',n')
i += 5
elif ytestdim%4 == 0:
while i < ytestdim:
print round(yTest[i],2),'t', round(yTest[i+1],2),'t', round(yTest[i+2],2),'t', round(yTest[i+3],2)
ftrain.write(str(round(yTest[i],2))+','+str(round(yTest[i+1],2))+','+str(round(yTest[i+2],2))+','+str(round(yTest[i+3],2))+',n')
i += 4
elif ytestdim%3 == 0 :
while i < ytestdim :
print round(yTest[i],2),'t', round(yTest[i+1],2),'t', round(yTest[i+2],2)
ftrain.write(str(round(yTest[i],2))+','+str(round(yTest[i+1],2))+','+str(round(yTest[i+2],2))+',n')
i += 3
elif ytestdim%2 == 0 :
while i < ytestdim :
print round(yTest[i],2), 't', round(yTest[i+1],2)
ftrain.write(str(round(yTest[i],2))+','+str(round(yTest[i+1],2))+',n')
i += 2
else :
while i< ytestdim :
print round(yTest[i],2)
ftrain.write(str(round(yTest[i],2))+',n')
i += 1
print "n"
# random forest grid search parameters
print "------------------- Begining Random Forest Grid Search -------------------"
rfparamgrid = {"n_estimators": [10], "max_features": ["auto", "sqrt", "log2"], "max_depth": [5,7]}
rf = RandomForestRegressor(random_state=0,n_jobs=2)
RfGridSearch = GridSearchCV(rf,param_grid=rfparamgrid,scoring='mean_squared_error',cv=10)
start = time()
RfGridSearch.fit(XTrain,yTrain)
# Get best random forest parameters
print("GridSearchCV took %.2f seconds for %d candidate parameter settings" %(time() - start,len(RfGridSearch.grid_scores_)))
RFtime = time() - start,len(RfGridSearch.grid_scores_)
#print(RfGridSearch.grid_scores_) # Diagnos
print("n_estimators = %d " % RfGridSearch.best_params_['n_estimators'])
ne = RfGridSearch.best_params_['n_estimators']
print("max_features = %s " % RfGridSearch.best_params_['max_features'])
mf = RfGridSearch.best_params_['max_features']
print("max_depth = %d " % RfGridSearch.best_params_['max_depth'])
md = RfGridSearch.best_params_['max_depth']
ftrain.write("Random Forest")
ftrain.write("RF search time, %s ,n" % (str(RFtime)))
ftrain.write("Number of Trees, %s ,n" % str(ne))
ftrain.write("Number of feature at split, %s ,n" % str(mf))
ftrain.write("Max depth of tree, %s ,n" % str(md))
# Train random forest and predict with optimised parameters
print("nn------------------- Starting opitimised RF training -------------------")
optRF = RandomForestRegressor(n_estimators = ne, max_features = mf, max_depth = md, random_state=0)
optRF.fit(XTrain, yTrain) # Train the model
RFfeatimp = optRF.feature_importances_
indices = np.argsort(RFfeatimp)[::-1]
print("Training R2 = %5.2f" % optRF.score(XTrain,yTrain))
print("Starting optimised RF prediction")
RFpreds = optRF.predict(XTest)
print("The predicted values now follow :")
RFpredsdim = RFpreds.shape[0]
i = 0
if RFpredsdim%5 == 0:
while i < RFpredsdim:
print round(RFpreds[i],2),'t', round(RFpreds[i+1],2),'t', round(RFpreds[i+2],2),'t', round(RFpreds[i+3],2),'t', round(RFpreds[i+4],2)
i += 5
elif RFpredsdim%4 == 0:
while i < RFpredsdim:
print round(RFpreds[i],2),'t', round(RFpreds[i+1],2),'t', round(RFpreds[i+2],2),'t', round(RFpreds[i+3],2)
i += 4
elif RFpredsdim%3 == 0 :
while i < RFpredsdim :
print round(RFpreds[i],2),'t', round(RFpreds[i+1],2),'t', round(RFpreds[i+2],2)
i += 3
elif RFpredsdim%2 == 0 :
while i < RFpredsdim :
print round(RFpreds[i],2), 't', round(RFpreds[i+1],2)
i += 2
else :
while i< RFpredsdim :
print round(RFpreds[i],2)
i += 1
print "n"
RFr2.append(optRF.score(XTest, yTest))
RFmse.append( metrics.mean_squared_error(yTest,RFpreds))
RFrmse.append(math.sqrt(RFmse[metcount]))
print ("Random Forest prediction statistics for fold %d are; MSE = %5.2f RMSE = %5.2f R2 = %5.2fnn" % (metcount+1, RFmse[metcount], RFrmse[metcount],RFr2[metcount]))
ftrain.write("Random Forest prediction statistics for fold %d are, MSE =, %5.2f, RMSE =, %5.2f, R2 =, %5.2f,nn" % (metcount+1, RFmse[metcount], RFrmse[metcount],RFr2[metcount]))
ffeatimp.write("Feature importance rankings from random forest,n")
for i in range(RFfeatimp.shape[0]) :
ffeatimp.write("%d. , feature %d , %s, (%f),n" % (i + 1, indices[i], npheader[indices[i]], RFfeatimp[indices[i]]))
# Store prediction in original order of data (itest) whilst following through the current test set order (j)
metcount += 1
ftrain.write("Fold %d, n" %(metcount))
print "------------------- Next Fold %d -------------------" %(metcount+1)
j = 0
for itest in test :
RFpredictions.append(RFpreds[j])
j += 1
lennames = names.shape[0]
lenpredictions = len(RFpredictions)
lentrue = y.shape[0]
if lennames == lenpredictions == lentrue :
fpred.write("Names/Label,, Prediction Random Forest,, True Value,n")
for i in range(0,lennames) :
fpred.write(str(names[i])+",,"+str(RFpredictions[i])+",,"+str(y[i])+",n")
else :
fpred.write("ERROR - names, prediction and true value array size mismatch. Dumping arrays for manual inspection in predictions.csvn")
fpred.write("Array printed in the order names/Labels, predictions RF and true valuesn")
fpred.write(names+"n")
fpred.write(RFpredictions+"n")
fpred.write(y+"n")
sys.exit("ERROR - names, prediction and true value array size mismatch. Dumping arrays for manual inspection in predictions.csv")
print "Final averaged Random Forest metrics : "
RFamse = sum(RFmse)/10
RFmse_sd = np.std(RFmse)
RFarmse = sum(RFrmse)/10
RFrmse_sd = np.std(RFrmse)
RFslope, RFintercept, RFr_value, RFp_value, RFstd_err = scipy.stats.linregress(RFpredictions, y)
RFR2 = RFr_value**2
print "Average Mean Squared Error = ", RFamse, " +/- ", RFmse_sd
print "Average Root Mean Squared Error = ", RFarmse, " +/- ", RFrmse_sd
print "R2 Final prediction against True values = ", RFR2
fpred.write("n")
fpred.write("FINAL PREDICTION STATISTICS,n")
fpred.write("Random Forest average MSE, %s, +/-, %s,n" %(str(RFamse), str(RFmse_sd)))
fpred.write("Random Forest average RMSE, %s, +/-, %s,n" %(str(RFarmse), str(RFrmse_sd)))
fpred.write("Random Forest slope, %s, Random Forest intercept, %s,n" %(str(RFslope), str(RFintercept)))
fpred.write("Random Forest standard error, %s,n" %(str(RFstd_err)))
fpred.write("Random Forest R, %s,n" %(str(RFr_value)))
fpred.write("Random Forest R2, %s,n" %(str(RFR2)))
ftrain.close()
fpred.close()
ffeatimp.close()
您还可以将功能选择添加到数据：
Sickit学习功能选择
Sickit学习提供了一些功能选择技术，您可以使用它来改善DM项目的某些方面