Pythonランダムフォレストとマシンの学習 - 改善

Question

私は機械学習のためにPythonを使用することに全く新しいです。私はFortranでのプログラミングの背景から来ています。あなたが想像しているように、Pythonはかなりの飛躍です。私は化学で働き、化学（化学にデータ科学技術を適用する）に携わるようになりました。そのため、パイソン大規模な機械学習ライブラリのアプリケーションが重要です。私もコードを効率的にする必要があります。私は実行し、OKを動作するように見えるコードを書いている。私が知りたいことは：

1どのように改善するのがより効率的になるか。

2私が使用していたものに対する代替配合に関する提案、可能であれば別の経路が優れている理由は？

私はデータと回帰の連続モデルを扱う傾向があります。

どのような提案もすばらしく、事前におねがいします。

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 labels\n')) 
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 models\n") 
     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 models\n") 
     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("\n\n------------------- 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 = %5.2f\n\n" % (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,\n\n" % (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.csv\n") 
       fpred.write("Array printed in the order names/Labels, predictions RF and true values\n") 
       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() 

また、あなたのデータへの機能の選択を追加することができます

Answer 1

：

sickit learn feature selection

技術が学び、あなたのDMプロジェクトのいくつかの側面を改善するために使用することができsickitで提供されるいくつかの特徴選択