def test_reddit(self):
### COMBINE COUNTS BY FAMILY ##############################################################
# Prepare behavioral terms
pickles = glob("output/*_dict_counts.pkl")
families = get_expanded_family_dict(unique=True)
# This is NOT a diagonal matrix, base terms are in rows, family members in columns
path_similarities = get_path_similarity_matrix()
for result_file in pickles:
tmp = pickle.load(open(result_file,"rb"))
print "Combining family counts for %s" %tmp["disorder"]
result = tmp["dfcount"]
# This will be a new matrix with only base terms as column names
familydf = pandas.DataFrame(index=result.index)
# Step 2: For each term stem (row), find family based on path similarity
for stem,data in families.iteritems():
family = path_similarities[stem][path_similarities[stem] != 0]
# Create a data frame with just the columns
column_names = [c for c in family.index if c in result.columns]
family = family[column_names]
# if there are no family members
if family.shape[0] == 0:
familydf[stem] = result[stem]
# Weight each count by the path similarity, and sum
else:
subset = result[column_names].copy()
for col in subset.columns:
subset[col] *= family[col]
familydf[stem] = subset.sum(axis=1) + result[stem]
# Save family data frame to file
tmp["familydf"] = familydf
pickle.dump(tmp,open(result_file.replace("dict_counts","dict_counts_family"),"wb"))
### 4. CO-OCCURRENCE ##################################################################
# Now calculate co-occurrence
terms = familydf.columns.tolist()
# Result df will be terms by terms
df = pandas.DataFrame(columns=terms,index=terms)
print "Calculating co-occurrence for %s" %tmp["disorder"]
for t in range(0,len(terms)):
term1 = terms[t]
subset = familydf.loc[familydf[term1]>0]
number_with_term1 = subset.shape[0]
if number_with_term1 != 0:
for term2 in terms:
number_with_term2 = subset[term2].loc[subset[term2]>0].shape[0]
pt2_given_t1 = float(number_with_term2) / number_with_term1
# [row](probability), [col](given)
df.loc[term2,term1] = pt2_given_t1
else:
df.loc[:,term1] = 0
df.to_csv("output/%s_co-occurrence.tsv" %tmp["disorder"],sep="\t")
print "Finished."
pass
os.system("Rscript prep_cooccurr_data.R")
# if on sherlock, need to load python2.7
# module load python/2.7.5
from brainbehavior.cognitiveatlas import get_expanded_family_dict, get_path_similarity_matrix
#from brainbehavior.utils import save_json
from brainbehavior.nlp import do_stem
#from textblob.wordnet import Synset
from glob import glob
import pandas
import pickle
# We want to first generate a matrix of relationships between all pairwise terms
families = get_expanded_family_dict()
# This is NOT a diagonal matrix, base terms are in rows, family members in columns
path_similarities = get_path_similarity_matrix(families)
# Load the result
result = pandas.read_pickle("/share/PI/russpold/work/PUBMED/pmc_behavior_counts.pkl")
# Step 1: Terms that appear in most papers need to be filterd out
percents = []
for c in result.columns:
col = result[c]
percent_occur = col[col!=0].shape[0] / float(col.shape[0])
percents.append(percent_occur)
percents = pandas.DataFrame(percents)
percents.index = result.columns
percents.to_csv("/share/PI/russpold/work/PUBMED/pmc_percent_occur.tsv",sep="\t")
# Lets try nixing words with > 0.4 frequency...
nix = percents.loc[percents[0]>0.1]
# if on sherlock, need to load python2.7
# module load python/2.7.5
from brainbehavior.cognitiveatlas import get_expanded_family_dict, get_path_similarity_matrix
#from brainbehavior.utils import save_json
from brainbehavior.nlp import do_stem
#from textblob.wordnet import Synset
from glob import glob
import pandas
import pickle
# We want to first generate a matrix of relationships between all pairwise terms
families = get_expanded_family_dict()
# This is NOT a diagonal matrix, base terms are in rows, family members in columns
path_similarities = get_path_similarity_matrix(families)
# Load the result
result = pandas.read_pickle(
"/share/PI/russpold/work/PUBMED/pmc_behavior_counts.pkl")
# Step 1: Terms that appear in most papers need to be filterd out
percents = []
for c in result.columns:
col = result[c]
percent_occur = col[col != 0].shape[0] / float(col.shape[0])
percents.append(percent_occur)
percents = pandas.DataFrame(percents)
percents.index = result.columns
percents.to_csv("/share/PI/russpold/work/PUBMED/pmc_percent_occur.tsv",
sep="\t")
# Only save if we have at least one!
if counts["count"].sum() > 0:
totalwords.append(get_total_words(text))
dfcount.loc[t,counts.index] = counts["count"]
result["dfcount"] = dfcount
result["words"] = totalwords
# Save to output file
pickle.dump(result,open(result_file.replace("dict","dict_counts"),"wb"))
### 3. COMBINE COUNTS BY FAMILY ##############################################################
# Prepare behavioral terms
pickles = glob("%s/*_dict_counts.pkl" %outfolder)
families = get_expanded_family_dict(unique=True)
# This is NOT a diagonal matrix, base terms are in rows, family members in columns
path_similarities = get_path_similarity_matrix()
for result_file in pickles:
tmp = pickle.load(open(result_file,"rb"))
print "Parsing disorder %s" %tmp["disorder"]
result = tmp["dfcount"]
# This will be a new matrix with only base terms as column names
familydf = pandas.DataFrame(index=result.index)
# Step 2: For each term stem (row), find family based on path similarity
for stem,data in families.iteritems():
family = path_similarities[stem][path_similarities[stem] != 0]
# Create a data frame with just the columns
column_names = [c for c in family.index if c in result.columns]
family = family[column_names]
# if there are no family members
if family.shape[0] == 0:
