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import numpy as np
import pandas as pd
import glob
from sklearn.decomposition import ProjectedGradientNMF
dontuse = ['SID','Name','Grader','Comments','Submission Time','Lateness','Question ID','Adjustment','Score']; #These are either irrelevant or hard to include in the analysis
path ='/Phys_7B_-_Lecture_2_Midterm_1' #Directory with the Gradescope files
offset = 0; #Makes sure files read correctly
allFiles = glob.glob(path + "/*.csv")
d = {'true':1.0,'false':0.0}; #Dictionaries to replace booleans with numbers
notd = {'true':0.0,'false':1.0};
init = False; #Track if this is the first iteration
ptvals = {}; #Stores point values of rubric items
for files in allFiles: #Loop over all problems
pth = files[files.rfind('/')+1:]; #Get the filename to track problem
if ("scores" in files): #The score file just gives us total score and emails
df = pd.read_csv(files,usecols=['Email','Total Score']);
df = pd.read_csv(files);
df = df.fillna(0);
tal = df.tail(3); #The last three lines have point information
df = df.drop(tal.index);
tal = np.array(tal);
if(tal[2,1] == "negative"):
sgn = -1.;
sgn = 1.;
usecols = ["Email"]; #Track which columns have useful information
for i in range(5-offset,len(df.columns)-offset):
if(df.columns[i+offset] not in dontuse):
pntval = float(tal[0,i])*sgn;
if(pntval>0): #If positive points, use column, use positive dictionary
col = df.columns[i+offset]+" "+pth;
used = d;
elif(pntval<0): #If negative points, use complement of column, use negative dictionary
col = "NOT "+df.columns[i+offset]+" "+pth;
pntval *= -1;
used = notd;
while(col in ptvals): #If grader gave a duplicate name to a rubric item, attach "A" to make it no longer duplicate
col += "A";
usecols.append(col); #Attach this new columns to the columns being used
df = df.rename(columns = {df.columns[i+offset]:col}); #Rename the column in df
df[col] = df[col].replace(used)*pntval; #Convert from boolean to points
ptvals[col] = pntval; #Store point values for rubric item
df = df[usecols]; #Only keep relevant columns
frame = pd.merge(frame,df,on='Email',how='left',suffixes=['',str(files)]) #If not first time, merge on emails
frame = df; #If first time, make the master dataframe
init = True;
frame = frame.sort('Total Score',ascending = False); #Sorting by Overall Score is interesting
fr = frame.drop('Email',1); #NMF will not use email or total score
fr = fr.drop('Total Score',1);
feature_names = fr.columns;
X = np.array(fr.astype(float));
'''for i in range(60): #Test error as a function of number of topics
model = ProjectedGradientNMF(n_components=i, init='nndsvda',random_state=0,max_iter=500)
print (i,model.reconstruction_err_);'''
model = ProjectedGradientNMF(n_components=11, init='nndsvda',random_state=0,max_iter=500) #Perform the NMF
Xtrans = model.fit_transform(X)
for topic_idx, topic in enumerate(model.components_): #Print the rubric items with strongest contribution in topics
sorte = np.sort(topic)[::-1];
sorteargs = np.argsort(topic)[::-1];
i = 0;
print("Topic #%d:" % topic_idx)
while(sorte[i]>1.5): #Only show things where contribution is large (1.5 is arbitrary)
print feature_names[sorteargs[i]],np.mean(np.transpose(X)[sorteargs[i]])/ptvals[feature_names[sorteargs[i]]];
i += 1;
sm = 0.;
nrm = 0.;
for k in range(len(topic)): #Find how many points are associated with the topic
sm += topic[k]*ptvals[feature_names[k]];
nrm += topic[k];
print sm/nrm;