def __init__(self, discrete_labels_fn, ppmi_fn, phrases_fn,
phrases_to_check_fn, fn):
ppmi = dt.importLabels(ppmi_fn)
ppmi = np.asarray(ppmi)
phrases = dt.importString(phrases_fn)
indexes_to_get = []
if phrases_to_check_fn != "":
phrases_to_check = dt.importString(phrases_to_check_fn)
for pc in range(len(phrases_to_check)):
for p in range(len(phrases)):
if phrases_to_check[pc] == phrases[p][6:]:
indexes_to_get.append(p)
ppmi = ppmi.transpose()
print len(ppmi), len(ppmi[0])
counter = 0
with open(discrete_labels_fn) as f:
for line in f:
exists = True
if phrases_to_check_fn != "":
exists = False
for i in indexes_to_get:
if i == counter:
exists = True
break
if exists:
discrete_labels = line.split()
saveGraph(discrete_labels, ppmi[counter],
fn + " " + phrases[counter][6:])
print phrases[counter]
counter += 1
def getMatchedLines(file_name, lines_to_match):
matched_lines = []
failed_lines = []
match_names = []
match_years = []
for line in lines_to_match:
match_names.append(re.split(r'\t+', line)[0])
match_years.append(re.split(r'\t+', line)[1])
file = open(file_name, "r")
lines = file.readlines()
for i in range(len(lines_to_match)):
matched = False
last_movie = ""
for l in range(len(lines)):
if matched is True and re.split(r'\t+', lines[l])[0] != last_movie:
break
split_line = re.split(r'\t+', lines[l])
split_line[0] = re.sub(r'\s+', '', split_line[0].translate(None, string.punctuation).lower())
match_names[i] = re.sub(r'\s+', '', match_names[i].translate(None, string.punctuation).lower())
if split_line[0] == match_names[i]:
matched_lines.append(lines[l])
matched = True
last_movie = re.split(r'\t+', lines[l])[0]
print "Found a line for " + last_movie
continue
if matched:
print "Matched", lines_to_match[i]
else:
failed_lines.append(lines_to_match[i])
print "Failed", lines_to_match[i]
dt.write1dArray(failed_lines, "filmdata/KeywordData/failed_second_match.txt")
dt.write1dArray(matched_lines, "filmdata/KeywordData/matched_lines_NEW.txt")
def getScoreDifferences(name_word_file1, name_score_file1, name_word_file2, name_score_file2, name):
word_file1 = open(name_word_file1, "r")
score_file1 = open(name_score_file1, "r")
word_lines1 = word_file1.readlines()
score_lines1 = score_file1.readlines()
scores1 = []
words1 = []
for s in score_lines1:
scores1.append(float(s.strip()))
for w in word_lines1:
words1.append(w.strip())
word_file2 = open(name_word_file2, "r")
score_file2 = open(name_score_file2, "r")
word_lines2 = word_file2.readlines()
score_lines2 = score_file2.readlines()
scores2 = []
words2 = []
for s in score_lines2:
scores2.append(float(s))
for w in word_lines2:
words2.append(w.strip())
differences_list = []
for i in range(len(score_lines1)):
differences_list.append(scores1[i] - scores2[i])
most_different_words = [x for (y,x) in sorted(zip(differences_list,words1))]
differences_list = sorted(differences_list)
dt.write1dArray(most_different_words, "filmdata/SVM/most_different_words_" + name + ".txt")
dt.write1dArray(differences_list, "filmdata/SVM/most_different_values_" + name + ".txt")
def getVectors(ordered_IDs, unique_phrases):
vectors = [[0 for x in range(len(ordered_IDs))] for x in range(len(unique_phrases))]
vectors_maintained = [[0 for x in range(len(ordered_IDs))] for x in range(len(unique_phrases))]
multi_dictionary = {}
dict_mapping = {}
print "Mapping to memory."
for i in range(len(ordered_IDs)):
ordered_IDs[i] = str(ordered_IDs[i])
for i in range(len(ordered_IDs)):
ordered_IDs[i] = ordered_IDs[i].strip()
dict_mapping[ordered_IDs[i]] = i
if ordered_IDs[i] != "-1":
file = open("filmdata/Tokens/" + ordered_IDs[i] + ".film", "r")
lines = file.readlines()[1:]
for line in lines:
split_line = line.split()
multi_dictionary[(ordered_IDs[i], split_line[0])] = int(split_line[1])
file.close()
else:
multi_dictionary[(ordered_IDs[i], split_line[0])] = 0
for up in range(len(unique_phrases)):
unique_phrases[up] = unique_phrases[up].strip()
print len("Iterating over memory.")
for p in range(13177, 25842, 1):
for key, value in multi_dictionary.iteritems():
if key[1] == unique_phrases[p]:
vectors_maintained[p][dict_mapping[key[0].strip()]] = value
vectors[p][dict_mapping[key[0]]] = 1
print unique_phrases[p]
dt.write1dArray(vectors_maintained[p], "filmdata/classesPhrases/nonbinary/class-" + unique_phrases[p])
dt.write1dArray(vectors[p], "filmdata/classesPhrases/class-" + unique_phrases[p])
return vectors_maintained, vectors
def __init__(self, discrete_labels_fn, ppmi_fn, phrases_fn, phrases_to_check_fn, fn):
ppmi = dt.importLabels(ppmi_fn)
ppmi = np.asarray(ppmi)
phrases = dt.importString(phrases_fn)
indexes_to_get = []
if phrases_to_check_fn != "":
phrases_to_check = dt.importString(phrases_to_check_fn)
for pc in range(len(phrases_to_check)):
for p in range(len(phrases)):
if phrases_to_check[pc] == phrases[p][6:]:
indexes_to_get.append(p)
ppmi = ppmi.transpose()
print len(ppmi), len(ppmi[0])
counter = 0
with open(discrete_labels_fn) as f:
for line in f:
exists = True
if phrases_to_check_fn != "":
exists = False
for i in indexes_to_get:
if i == counter:
exists = True
break
if exists:
discrete_labels = line.split()
saveGraph(discrete_labels, ppmi[counter], fn + " " + phrases[counter][6:])
print phrases[counter]
counter += 1
def findMissingKeywords(file_name, common_keywords):
print "?"
file = open(file_name, "r")
lines = file.readlines()
last_film = ""
movie_strings = dt.importString("filmdata/filmNames.txt")
standard_strings = []
indexes = []
for m in movie_strings:
m = m[:-5]
standard_strings.append(m.translate(None, string.punctuation).replace(" ", "").strip().upper())
for line in lines:
film_vectors = []
line = line.strip()
if len(line) > 2:
line_split = re.split(r'\t+', line)
line_split[0] = line_split[0].translate(None, string.punctuation).replace(" ", "").strip().upper()
file_save = ""
for m in range(len(standard_strings)):
if standard_strings[m] == line_split[0]:
print "matched", m, standard_strings[m], line_split[0]
file_save = str(m)
break
if file_save != "":
if last_film.strip() != line_split[0].strip() and last_film is not None:
print "Succeeded", line_split[0]
for m in range(len(standard_strings)):
if standard_strings[m] == last_film:
indexes.append(m)
break
last_film = line_split[0]
else:
print "Failed", line_split[0],
dt.write1dArray(indexes, "filmdata/MISSING_FROM_MOVIEDATA.txt")
def getIDs(movie_strings):
ordered_IDs = []
movie_names = []
for name in movie_strings:
movie_names.append(name[:-5])
id_mappings = open("filmdata/KeywordData/Movie_Most_Common_Keyword_Mapping/films-ids.txt", "r")
id_mappings_lines = id_mappings.readlines()
found_name = False
failed_names = []
x = 0
for name in movie_names:
for line in id_mappings_lines:
mapping_id = line.split()[0]
mapping_name = re.split(r'\t+', line)[2]
if similar(name.upper().strip(), mapping_name.upper().strip()):
ordered_IDs.append(mapping_id)
found_name = True
break
if found_name is True:
found_name = False
else:
failed_names.append(name)
ordered_IDs.append(-1)
x += 1
print x
dt.write1dArray(failed_names, "filmdata/KeywordData/NAMES_THAT_FAILED_IDS.txt")
dt.write1dArray(ordered_IDs, "filmdata/KeywordData/IDsByOriginalOrdering.txt")
def outputTopByVotes(amount_of_votes):
file = open("filmdata/ratings.list/ratings.list", "r")
lines = file.readlines()
top_movies = []
top_ratings = []
for line in lines:
top_ratings.append(int(line.split()[1]))
top_ratings = np.asarray(top_ratings)
indices = np.argpartition(top_ratings, -amount_of_votes)[-amount_of_votes:]
for i in indices:
just_movie = lines[i].split()[3:]
just_movie = " ".join(just_movie)
just_movie = just_movie.split('{')
just_movie = just_movie[0]
just_movie = just_movie.split('(')
try:
if not re.findall(r'\d+', just_movie[2])[0]:
del just_movie[2]
else:
just_movie[0] = just_movie[0] + "(" + just_movie[1]
del just_movie[1]
except IndexError:
print
try:
year = re.findall(r'\d+', just_movie[1])[0]
except IndexError:
print "FALED", just_movie
if just_movie[0].endswith(' '):
just_movie[0] = just_movie[0][:-1]
if just_movie[0].startswith('"') and just_movie[0].endswith('"'):
just_movie[0] = just_movie[0][1:-1]
just_movie = just_movie[0] + " " + str(year)
print just_movie
top_movies.append(just_movie)
dt.write1dArray(top_movies, "filmdata/top50000moviesbyvotes.txt")
def getVectorsKeywords(movie_strings, keywords):
multi_dictionary = {}
dict_mapping = {}
movie_names = []
file_names = dt.getAllFileNames("filmdata\KeywordData\Movie_Most_Common_Keyword_Mapping")
for i in movie_strings:
movie_names.append(i.strip()[:-5])
print i
print "Mapping to memory."
for i in file_names:
try:
file = open("filmdata/KeywordData/Movie_Most_Common_Keyword_Mapping/" +i, "r")
lines = file.readlines()
dict_mapping[movie_strings[int(i)]] = i
for line in lines:
line = line.strip()
multi_dictionary[(movie_strings[int(i)], line)] = 1
file.close()
except IOError:
print movie_names[i]
for up in range(len(keywords)):
keywords[up] = keywords[up].strip()
print len("Iterating over memory.")
for p in range(len(keywords)):
vector = [0 for x in range(len(movie_strings))]
print len(vector)
for key, value in multi_dictionary.iteritems():
if key[1] == keywords[p]:
#print int(dict_mapping[key[0]])
vector[int(dict_mapping[key[0]])] = 1
print keywords[p]
dt.write1dArray(vector, "filmdata/classesKeywords/NewData/class-" + keywords[p])
def outputKeywords():
movie_strings = dt.importString("filmdata/filmNames.txt")
movie_data = getMovieDataFromIMDB(movie_strings)
commonality = 0
common_keywords = getMostCommonKeywords(0, "filmdata/IMDB_movie_data.txt")
dt.write1dArray(common_keywords, "filmdata/common_keywords_15k_commanility_" + str(commonality))
vectors = getKeywordVectors(common_keywords, movie_strings, "")
dt.write2dArray(vectors, "filmdata/classesKeywords/class-extra-all-commonality-" + str(commonality))
def trimMovieData(file_string):
new_movie_data = []
movie_data_file = open(file_string)
with movie_data_file as myFile:
for num, line in enumerate(myFile, 1):
if "{" not in line and "(V)" not in line and "(TV)" not in line and "(VG)" not in line and len(line) > 2 and line.startswith(" ") is False and line.startswith("\t") is False and line.startswith("\n") is False:
new_movie_data.append(line[:-1])
print "dun"
dt.write1dArray(new_movie_data, file_string + "trimmed")
def getMissingIndexes(index_list, length):
full_index = range(length)
for i in index_list:
i = int(i)
full_index[i] = -1
missing_indexes = []
for i in full_index:
if i > -1:
missing_indexes.append(i)
dt.write1dArray(missing_indexes, "filmdata/missing_indexes_keywords.txt")
def getIMDBKeywordsForMovieNames(movie_names):
stripped_movie_names = []
for movie in movie_names:
stripped_movie_names.append(movie.replace('\n', ''))
stripped_movie_names = sorted(stripped_movie_names)
split_names = []
split_years = []
for stripped_movie_name in stripped_movie_names:
split = stripped_movie_name.split()
split_year = split[len(split)-1]
split_years.append(split_year)
split_names.append(stripped_movie_name[:-len(split_year)-1])
file = open("filmdata\keywords.list\keywords.list", "r")
lines = file.readlines()
keywords_list = lines[79748:]
matched_lines = []
x = 0
last_line = keywords_list[0]
matched = False
while x < 50000:
for line in keywords_list:
split_line = line.rsplit('(', 2)
movie_name = split_line[0].rstrip()
if movie_name.startswith('"') and movie_name.endswith('"'):
movie_name = movie_name[1:-1]
try:
movie_year = str(re.findall(r'\d+', split_line[1])[0])
except IndexError:
movie_year = "NULL"
if not movie_name:
movie_name = "'NULL"
formatted_line = movie_name.rstrip() + " " + str(movie_year).rstrip()
if matched is True and formatted_line == last_line:
matched_lines.append(line)
print split_names[x], line
elif matched is False and similar(movie_name.strip().upper(), split_names[x].strip().upper()) and movie_year == split_years[x]:
matched = True
matched_lines.append(line)
print split_names[x], line
elif matched is True and formatted_line != last_line:
matched = False
x = x + 1
last_line = formatted_line
print "cycled through"
print "Found:", x
dt.write1dArray(matched_lines, "filmdata/imdb_movie_keywords.txt")
def getXLeastSimilarIndex(term, terms_to_match, terms_to_ignore, amt):
least_similar_term_indexes = []
for a in range(amt):
lowest_term = 99999999
term_index = 0
for t in range(len(terms_to_match)):
if dt.checkIfInArray(terms_to_ignore, t) is False:
s = getSimilarity(term, terms_to_match[t])
if s < lowest_term and dt.checkIfInArray(least_similar_term_indexes, t) is False:
lowest_term = s
term_index = t
least_similar_term_indexes.append(term_index)
return least_similar_term_indexes
def getXMostSimilarIndex(term, terms_to_match, terms_to_ignore, amt):
most_similar_term_indexes = []
for a in range(amt):
highest_term = 0
term_index = 0
for t in range(len(terms_to_match)):
if dt.checkIfInArray(terms_to_ignore, t) is False:
s = getSimilarity(term, terms_to_match[t])
if s > highest_term and dt.checkIfInArray(most_similar_term_indexes, t) is False:
highest_term = s
term_index = t
most_similar_term_indexes.append(term_index)
return most_similar_term_indexes
def __init__(self, directions_fn, vectors_fn, cluster_names_fn,
vector_names_fn, fn, percent, percentage_increment,
by_vector):
directions = dt.importVectors(directions_fn)
vectors = dt.importVectors(vectors_fn)
cluster_names = dt.importString(cluster_names_fn)
vector_names = dt.importString(vector_names_fn)
rankings = self.getRankings(directions, vectors, cluster_names,
vector_names)
rankings = np.array(rankings)
#labels = self.createLabels(rankings, percent)
#labels = np.asarray(labels)
discrete_labels = self.createDiscreteLabels(rankings,
percentage_increment)
discrete_labels = np.asarray(discrete_labels)
if by_vector:
#labels = labels.transpose()
discrete_labels = discrete_labels.transpose()
rankings = rankings.transpose()
#dt.write2dArray(labels, "Rankings/" + fn + "P" + str(percent) +".labels")
dt.write2dArray(rankings, "Rankings/" + fn + ".space")
dt.write2dArray(
discrete_labels,
"Rankings/" + fn + "P" + str(percentage_increment) + ".discrete")
array = []
short_array = []
""" Disabled names for quick view now
def getXMostSimilarIndex(term, terms_to_match, terms_to_ignore, amt):
most_similar_term_indexes = []
for a in range(amt):
highest_term = 0
term_index = 0
for t in range(len(terms_to_match)):
if dt.checkIfInArray(terms_to_ignore, t) is False:
s = getSimilarity(term, terms_to_match[t])
if s > highest_term and dt.checkIfInArray(
most_similar_term_indexes, t) is False:
highest_term = s
term_index = t
most_similar_term_indexes.append(term_index)
return most_similar_term_indexes
def getXLeastSimilarIndex(term, terms_to_match, terms_to_ignore, amt):
least_similar_term_indexes = []
for a in range(amt):
lowest_term = 99999999
term_index = 0
for t in range(len(terms_to_match)):
if dt.checkIfInArray(terms_to_ignore, t) is False:
s = getSimilarity(term, terms_to_match[t])
if s < lowest_term and dt.checkIfInArray(
least_similar_term_indexes, t) is False:
lowest_term = s
term_index = t
least_similar_term_indexes.append(term_index)
return least_similar_term_indexes
def getUnformattedTopByVotes(amount_of_votes):
file = open("filmdata/ratings.list/ratings.list", "r")
lines = file.readlines()
top_movies = []
top_ratings = []
for line in lines:
top_ratings.append(int(line.split()[1]))
top_ratings = np.asarray(top_ratings)
indices = np.argpartition(top_ratings, -amount_of_votes)[-amount_of_votes:]
for i in indices:
just_movie = lines[i].split()[3:]
just_movie = " ".join(just_movie)
print just_movie
top_movies.append(just_movie)
dt.write1dArray(top_movies, "filmdata/imdb_formatted_top50000.txt")
def reMapPPMI(ordered_IDs, file_names):
print "Mapping to memory."
for i in range(len(ordered_IDs)):
ordered_IDs[i] = str(ordered_IDs[i])
for i in range(len(ordered_IDs)):
for f in range(len(file_names)):
id = file_names[f].split(".")[0]
if int(ordered_IDs[i]) == int(id) and int(ordered_IDs[i]) != -1:
print ordered_IDs[i], id
file = open("filmdata/vectors/Tokens/" + file_names[f])
lines = file.readlines()
dt.write1dArray(lines, "filmdata/NewTokens/"+str(i)+".ppmi")
file.close()
elif int(ordered_IDs[i]) == -1:
dt.write1dArray([[""]], "filmdata/NewTokens/"+str(i)+".error")
def makeConsistent(file_name, new_file_name):
new_file = []
with open(file_name) as my_file:
for num, line in enumerate(my_file, 1):
line = line.strip()
name = line[:-4]
year = line[len(line)-4:]
name = name.translate(None, string.punctuation)
year = year.translate(None, string.punctuation)
new_line = "\t".join([name, year])
new_file.append(new_line)
print new_line
dt.write1dArray(new_file, new_file_name)
def makeKeywordPPMIVectors(file_name, common_keywords):
print "?"
file = open(file_name, "r")
lines = file.readlines()
last_film = ""
movie_strings = dt.importString("filmdata/filmNames.txt")
standard_strings = []
for m in movie_strings:
m = m[:-5]
standard_strings.append(m.translate(None, string.punctuation).replace(" ", "").strip().upper())
for line in lines:
film_vectors = []
line = line.strip()
if len(line) > 2:
line_split = re.split(r'\t+', line)
line_split[0] = line_split[0].translate(None, string.punctuation).replace(" ", "").strip().upper()
file_save = ""
for m in range(len(standard_strings)):
if standard_strings[m] == line_split[0]:
file_save = str(m)
break
if file_save != "":
file = open("filmdata\KeywordData\Movie_Most_Common_Keyword_Mapping\\" + file_save, "a")
for keyword in common_keywords:
if line_split[2] == keyword.strip():
film_vectors.append("line")
file.write(keyword)
break
if last_film.strip() != line_split[0].strip() and last_film is not None:
print "Succeeded", line_split[0]
file.close()
last_film = line_split[0]
else:
print "Failed", line_split[0]
def getKNeighbors(vector_path="filmdata/films200.mds/films200.mds", class_path="filmdata/classesGenres/class-All",
n_neighbors=1, algorithm="kd_tree", leaf_size=30,
training_data=10000, name="normal200"):
movie_vectors = np.asarray(dt.importVectors(vector_path))
movie_labels = np.asarray(dt.importLabels(class_path))
x_train, y_train, x_test, y_test = dt.splitData(training_data, movie_vectors, movie_labels)
classifier = neighbors.KNeighborsClassifier(n_neighbors=n_neighbors, algorithm=algorithm, leaf_size=leaf_size)
classifier.fit(x_train, y_train.ravel())
y_pred = classifier.predict(x_test)
f1 = f1_score(y_test, y_pred, average='macro')
accuracy = accuracy_score(y_test, y_pred)
dt.write1dArray([f1, accuracy], "KNNScores/" + name + ".score")
print "F1 " + str(f1), "Accuracy", accuracy
def writeMissing(folder_name):
print "?"
file_names = dt.getAllFileNames(folder_name)
standard = range(15000)
missing = []
for i in standard:
found = False
for f in file_names:
if int(f) == int(i):
found = True
break
if found:
print "found", i
else:
missing.append(i)
print "no found", i
dt.write1dArray(missing, "filmdata/MISSING_KEYWORD_ITEMS.txt")
def get_code(self, tree, feature_names, class_names, filename):
left = tree.tree_.children_left
right = tree.tree_.children_right
threshold = tree.tree_.threshold
value = tree.tree_.value
#print tree.tree_.feature, len(tree.tree_.feature
# )
features = []
for i in tree.tree_.feature:
if i != -2 or i <= 200:
features.append(feature_names[i])
rules_array = []
def recurse(left, right, threshold, features, node):
if (threshold[node] != -2):
line = "IF ( " + features[node] + " <= " + str(
threshold[node]) + " ) {"
rules_array.append(line)
if left[node] != -1:
recurse(left, right, threshold, features, left[node])
line = "} ELSE {"
rules_array.append(line)
if right[node] != -1:
recurse(left, right, threshold, features, right[node])
line = "}"
rules_array.append(line)
else:
if value[node][0][0] >= value[node][0][1]:
line = "return", class_names[0]
rules_array.append(line)
else:
line = "return", class_names[1]
rules_array.append(line)
recurse(left, right, threshold, features, 0)
dt.write1dArray(rules_array, "Rules/Statements/" + filename + ".rules")
cleaned = jsbeautifier.beautify_file("Rules/Statements/" + filename +
".rules")
file = open("Rules/Statements/" + filename + ".rules", "w")
file.write(cleaned)
file.close()
def getMostCommonKeywords(top_value, file_name, keyword_file, value_file):
common_keywords = []
file = open(file_name, "r")
lines = file.readlines()
keywords = defaultdict(int)
for line in lines:
if len(line.split()) > 0:
line_split = line.split()
keyword = line_split[len(line_split)-1]
keywords[keyword] += 1
print line
sorted_dict = sorted(keywords.iteritems(), key=lambda x:-x[1])[:top_value]
print sorted_dict
keys = []
values = []
for key, value in sorted_dict:
keys.append(key)
values.append(value)
dt.write1dArray(keys, keyword_file)
dt.write1dArray(values, value_file)
def getKNearestMovies(data, x, k):
movie_names = dt.importString("filmdata/filmNames.txt")
kd_tree = spatial.KDTree(data)
kd_query = kd_tree.query(x=x, k=k)
nearest_distances = kd_query[0][1:]
k_nearest = kd_query[1][1:]
nearest_movies = []
for k in k_nearest:
nearest_movies.append(movie_names[k].strip())
print nearest_movies
return nearest_movies, nearest_distances
def getKNearestMovies(data, x, k):
movie_names = dt.importString("filmdata/filmNames.txt")
kd_tree = spatial.KDTree(data)
kd_query = kd_tree.query(x=x, k=k)
nearest_distances = kd_query[0][1:]
k_nearest = kd_query[1][1:]
nearest_movies = []
for k in k_nearest:
nearest_movies.append(movie_names[k].strip())
print nearest_movies
return nearest_movies, nearest_distances
def __init__(self,
epochs=1, learn_rate=0.01, loss="mse", batch_size=1, decay=1e-06,
hidden_activation="tanh", layer_init="glorot_uniform", output_activation="tanh", hidden_layer_size=100,
file_name="unspecified_filename", vector_path=None, reg=0,
optimizer_name="rmsprop", class_names=None, noise=0, output_weights=None):
# Initialize the model
self.model = Sequential()
# Import the numpy vectors
try:
movie_vectors = np.asarray(np.load(vector_path))
except OSError:
# If it fails, assume that it's in a standard format for vectors and then save it in numpy format
movie_vectors = dt.importVectors(vector_path)
movie_vectors = np.asarray(movie_vectors)
np.save(file_name, movie_vectors)
# Set the input and the output to be the same size, as this is an auto-encoder
input_size = len(movie_vectors[0])
output_size = len(movie_vectors[0])
if noise > 0: # If using a noisy autoencoder, add GaussianNoise layers to the start of the encoder
self.model.add(GaussianNoise(noise, input_shape=(input_size,)))
self.model.add(Dense(output_dim=hidden_layer_size, input_dim=input_size, init=layer_init, activation=hidden_activation,W_regularizer=l2(reg)))
else:
# Otherwise just add the hidden layer
self.model.add(Dense(output_dim=hidden_layer_size, input_dim=input_size, init=layer_init, activation=hidden_activation,W_regularizer=l2(reg)))
# If using custom weights on the hidden layer to the output layer, apply those custom weights. Otherwise just add output layer.
if output_weights == None:
self.model.add(Dense(output_dim=output_size, init=layer_init, activation=output_activation))
else:
self.model.add(Dense(output_dim=len(output_weights[0]), init=layer_init, activation=output_activation, weights=output_weights))
# Compile the model and fit it to the data
if optimizer_name == "sgd":
optimizer = SGD(lr=learn_rate, decay=decay)
elif optimizer_name == "rmsprop":
optimizer = RMSprop(lr=learn_rate)
self.model.compile(loss=loss, optimizer=optimizer)
self.model.fit(movie_vectors, movie_vectors, nb_epoch=epochs, batch_size=batch_size, verbose=1)
# Create a truncated model that has no output layer that has the same weights as the previous model and use it to obtain the hidden layer representation
truncated_model = Sequential()
total_file_name = "newdata/spaces/" + file_name +".mds"
truncated_model.add(GaussianNoise(noise, input_shape=(input_size,)))
truncated_model.add(Dense(output_dim=hidden_layer_size, input_dim=input_size, init=layer_init, activation=hidden_activation, W_regularizer=l2(reg)))
truncated_model.compile(loss=loss, optimizer=optimizer)
self.end_space = truncated_model.predict(movie_vectors)
np.save(self.end_space, total_file_name)
def splitDirections(self, directions_fn, scores_fn, names_fn,
low_threshold, high_threshold):
directions = dt.importVectors(directions_fn)
scores = dt.importString(scores_fn)
names = dt.importString(names_fn)
for s in range(len(scores)):
scores[s] = float(scores[s].strip())
high_direction_indexes = []
high_direction_scores = []
low_direction_indexes = []
low_direction_scores = []
for s in range(len(scores)):
if scores[s] >= high_threshold:
high_direction_indexes.append(s)
high_direction_scores.append(scores[s])
elif scores[s] >= low_threshold:
low_direction_indexes.append(s)
low_direction_scores.append(scores[s])
sorted_h_indexes = dt.sortByArray(high_direction_indexes,
high_direction_scores)
sorted_l_indexes = dt.sortByArray(low_direction_indexes,
low_direction_scores)
sorted_h_indexes.reverse()
sorted_l_indexes.reverse()
high_direction_names = []
low_direction_names = []
high_directions = []
low_directions = []
for s in sorted_h_indexes:
high_directions.append(directions[s])
high_direction_names.append(names[s][6:])
for s in sorted_l_indexes:
low_directions.append(directions[s])
low_direction_names.append(names[s][6:])
return high_direction_names, low_direction_names, high_directions, low_directions
def __init__(self, cluster_vectors_fn, cluster_labels_fn, movie_names_fn,
label_names_fn, cluster_names_fn, filename, training_data,
cluster_to_classify, max_depth):
vectors = dt.importVectors(cluster_vectors_fn)
labels = dt.importLabels(cluster_labels_fn)
cluster_names = dt.importString(cluster_names_fn)
vector_names = dt.importString(movie_names_fn)
label_names = dt.importString(label_names_fn)
scores_array = []
for l in range(len(labels[0])):
new_labels = [0] * 15000
for x in range(len(labels)):
new_labels[x] = labels[x][l]
x_train = np.asarray(vectors[:training_data])
x_test = np.asarray(vectors[training_data:])
y_train = np.asarray(new_labels[:training_data])
y_test = np.asarray(new_labels[training_data:])
self.clf = tree.DecisionTreeClassifier(max_depth=max_depth)
self.clf = self.clf.fit(x_train, y_train)
y_pred = self.clf.predict(x_test)
f1 = f1_score(y_test, y_pred, average='binary')
accuracy = accuracy_score(y_test, y_pred)
scores = [[label_names[l], "f1", f1, "accuracy", accuracy]]
print scores[0]
scores_array.append(scores)
class_names = [label_names[l], "NOT " + label_names[l]]
tree.export_graphviz(self.clf,
feature_names=cluster_names,
class_names=class_names,
out_file='Rules/' + label_names[l] +
filename + '.dot',
max_depth=10)
"""
rewrite_dot_file = dt.importString('Rules/'+filename+label_names[l]+'.dot')
new_dot_file = []
for s in rewrite_dot_file:
new_string = s
if "->" not in s and "digraph" not in s and "node" not in s and "(...)" not in s and "}" not in s:
index = s.index("value")
new_string = s[:index] + '"] ;'
new_dot_file.append(new_string)
dt.write1dArray(new_dot_file, 'Rules/Cleaned'+filename+label_names[l]+'.dot')
"""
graph = pydot.graph_from_dot_file('Rules/' + label_names[l] +
filename + '.dot')
graph.write_png('Rules/Images/' + label_names[l] + filename +
".png")
self.get_code(self.clf, cluster_names, class_names,
label_names[l] + filename)
dt.write1dArray(scores_array, 'Rules/Scores/' + filename + '.scores')
def getMovieDataFromIMDB(movie_strings):
movie_data = []
write_line_file = open("filmdata/Found_Missing.txt", "w")
failed_movies = []
names = []
years = []
for movie_string in movie_strings:
names.append(movie_string[:-6])
years.append(movie_string[-5:].strip())
for n in range(len(names)):
found = False
last_name = ""
old_num = 0
with open("filmdata/IMDB_movie_data.txttrimmed") as myFile:
for num, line in enumerate(myFile, 1):
num = old_num
split_line = re.split(r'\t+', line)
movie_string = split_line[0]
movie_name = movie_string.split()
del movie_name[len(movie_name)-1]
movie_name = " ".join(movie_name)
if found is True and last_name != movie_name:
break
movie_year = int(re.findall(r'\d+', movie_string.split()[len(movie_string.split())-1])[0])
if similar(names[n].upper().strip(), movie_name.upper().strip()) and int(years[n]) == int(movie_year):
movie_data.append(line)
write_line_file.write(line)
found = True
old_num = num
last_name = movie_name
if found is False:
failed_movies.append(names[n])
print "FAILED:", names[n]
print "Total failed", len(failed_movies)
write_line_file.close()
dt.write1dArray(failed_movies, "filmdata/failed_movies_final_push.txt")
return movie_data
def get_code(self, tree, feature_names, class_names, filename):
left = tree.tree_.children_left
right = tree.tree_.children_right
threshold = tree.tree_.threshold
value = tree.tree_.value
#print tree.tree_.feature, len(tree.tree_.feature
# )
features = []
for i in tree.tree_.feature:
if i != -2 or i <= 200:
features.append(feature_names[i])
rules_array = []
def recurse(left, right, threshold, features, node):
if (threshold[node] != -2):
line = "IF ( " + features[node] + " <= " + str(threshold[node]) + " ) {"
rules_array.append(line)
if left[node] != -1:
recurse (left, right, threshold, features,left[node])
line = "} ELSE {"
rules_array.append(line)
if right[node] != -1:
recurse (left, right, threshold, features,right[node])
line = "}"
rules_array.append(line)
else:
if value[node][0][0] >= value[node][0][1]:
line = "return", class_names[0]
rules_array.append(line)
else:
line = "return", class_names[1]
rules_array.append(line)
recurse(left, right, threshold, features, 0)
dt.write1dArray(rules_array, "Rules/Statements/"+filename+".rules")
cleaned = jsbeautifier.beautify_file("Rules/Statements/"+filename+".rules")
file = open("Rules/Statements/"+filename+".rules", "w")
file.write(cleaned)
file.close()
def getNextClusterTerm(cluster_terms, terms_to_match, terms_to_ignore, amt):
min_value = 999999999999999
min_index = 0
for t in range(len(terms_to_match)):
max_value = 0
if dt.checkIfInArray(terms_to_ignore, t) is False:
for c in range(len(cluster_terms)):
s = getSimilarity(cluster_terms[c], terms_to_match[t])
if s > max_value:
max_value = s
if max_value < min_value:
min_value = max_value
min_index = t
return min_index
def getNextClusterTerm(cluster_terms, terms_to_match, terms_to_ignore, amt):
min_value = 999999999999999
min_index = 0
for t in range(len(terms_to_match)):
max_value = 0
if dt.checkIfInArray(terms_to_ignore, t) is False:
for c in range(len(cluster_terms)):
s = getSimilarity(cluster_terms[c], terms_to_match[t])
if s > max_value:
max_value = s
if max_value < min_value:
min_value = max_value
min_index = t
return min_index
def makeConsistentKeywords(file_name, new_file_name):
new_file = []
with open(file_name) as my_file:
for num, line in enumerate(my_file, 1):
if "{" in line or "?" in line:
continue
split_line = re.split(r'\t+', line)
split_on_bracket = split_line[0].split(" (")
if split_on_bracket[1].startswith("1") == False and split_on_bracket[1].startswith("2") == False:
year = split_on_bracket[2][:4]
name = "".join([split_on_bracket[0], split_on_bracket[1]])
else:
year = split_on_bracket[1][:4]
name = split_on_bracket[0]
name = name.translate(None, string.punctuation)
year = year.translate(None, string.punctuation)
keyword = re.sub(r'\s+', '', split_line[1]).translate(None, string.punctuation)
name_and_year = "\t".join([name, year])
new_line = "\t".join([name_and_year, keyword])
new_file.append(new_line)
print new_line
dt.write1dArray(new_file, new_file_name)
def getVectorsIO(ordered_IDs, unique_phrases):
vectors = [[0 for x in range(len(ordered_IDs))] for x in range(len(unique_phrases))]
vectors_maintained = [[0 for x in range(len(ordered_IDs))] for x in range(len(unique_phrases))]
for p in range(11212, 25842, 1):
unique_phrases[p] = unique_phrases[p].strip()
for i in range(len(ordered_IDs)):
ordered_IDs[i] = ordered_IDs[i].strip()
if ordered_IDs[i] != "-1":
file = open("filmdata/Tokens/" + ordered_IDs[i] + ".film", "r")
lines = file.readlines()[1:]
for line in lines:
split_line = line.split()
split_line[1] = split_line[1].strip()
if split_line[0] == p:
vectors_maintained[p][i] = split_line[1]
vectors[p][i] = 1
file.close()
print unique_phrases[p]
dt.write1dArray(vectors_maintained[p], "filmdata/classesPhrases/nonbinary/class-" + unique_phrases[p])
dt.write1dArray(vectors[p], "filmdata/classesPhrases/class-" + unique_phrases[p])
return vectors_maintained, vectors
def splitDirections(self, directions_fn, scores_fn, names_fn, low_threshold, high_threshold):
directions = dt.importVectors(directions_fn)
scores = dt.importString(scores_fn)
names = dt.importString(names_fn)
for s in range(len(scores)):
scores[s] = float(scores[s].strip())
high_direction_indexes = []
high_direction_scores = []
low_direction_indexes = []
low_direction_scores = []
for s in range(len(scores)):
if scores[s] >= high_threshold:
high_direction_indexes.append(s)
high_direction_scores.append(scores[s])
elif scores[s] >= low_threshold:
low_direction_indexes.append(s)
low_direction_scores.append(scores[s])
sorted_h_indexes = dt.sortByArray(high_direction_indexes, high_direction_scores)
sorted_l_indexes = dt.sortByArray(low_direction_indexes , low_direction_scores)
sorted_h_indexes.reverse()
sorted_l_indexes.reverse()
high_direction_names = []
low_direction_names = []
high_directions = []
low_directions = []
for s in sorted_h_indexes:
high_directions.append(directions[s])
high_direction_names.append(names[s][6:])
for s in sorted_l_indexes:
low_directions.append(directions[s])
low_direction_names.append(names[s][6:])
return high_direction_names, low_direction_names, high_directions, low_directions
def getKNeighbors(vector_path="filmdata/films200.mds/films200.mds",
class_path="filmdata/classesGenres/class-All",
n_neighbors=1,
algorithm="kd_tree",
leaf_size=30,
training_data=10000,
name="normal200"):
movie_vectors = np.asarray(dt.importVectors(vector_path))
movie_labels = np.asarray(dt.importLabels(class_path))
x_train, y_train, x_test, y_test = dt.splitData(training_data,
movie_vectors,
movie_labels)
classifier = neighbors.KNeighborsClassifier(n_neighbors=n_neighbors,
algorithm=algorithm,
leaf_size=leaf_size)
classifier.fit(x_train, y_train.ravel())
y_pred = classifier.predict(x_test)
f1 = f1_score(y_test, y_pred, average='macro')
accuracy = accuracy_score(y_test, y_pred)
dt.write1dArray([f1, accuracy], "KNNScores/" + name + ".score")
print "F1 " + str(f1), "Accuracy", accuracy
def __init__(self, name_distinction="", class_names=None, vector_path=None, class_path=None, class_by_class=True, input_size=200,
training_data=10000, amount_of_scores=400, low_kappa=0.1, high_kappa=0.5, rankSVM=False, amount_to_cut_at=100, largest_cut=21470000):
print "getting movie data"
movie_vectors = dt.importVectors(vector_path)
movie_labels = dt.importLabels(class_path)
print "getting file names"
file_names = dt.getFns(class_path[:-10])
print len(movie_labels), len(movie_labels[0])
print "getting training and test data"
x_train = np.asarray(movie_vectors[:training_data])
x_test = np.asarray(movie_vectors[training_data:])
movie_labels = zip(*movie_labels)
file_names, movie_labels = self.getSampledData(file_names, movie_labels, amount_to_cut_at, largest_cut)
movie_labels = zip(*movie_labels)
y_train = movie_labels[:training_data]
y_test = movie_labels[training_data:]
y_train = np.asarray(zip(*y_train))
y_test = np.asarray(zip(*y_test))
print len(y_train), len(y_test), training_data
print "getting kappa scores"
kappa_scores, directions = self.runAllSVMs(y_test, y_train, x_train, x_test, file_names)
dt.write1dArray(kappa_scores, "SVMResults/"+name_distinction+".scores")
dt.write1dArray(file_names, "SVMResults/"+name_distinction+".names")
dt.write2dArray(directions, "directions/"+name_distinction+".directions")
def __init__(self, low_threshold, high_threshold, filename):
hdn, ldn, hd, ld = self.splitDirections(
"Directions/" + filename + ".directions",
"SVMResults/" + filename + ".scores",
"SVMResults/" + filename + ".names", low_threshold, high_threshold)
least_similar_cluster_names, cluster_name_dict, least_similar_clusters = self.createTermClusters(
hd, ld, hdn, ldn)
dt.write1dArray(
least_similar_cluster_names,
"Clusters/" + filename + "LeastSimilarHIGH" + str(high_threshold) +
"," + str(low_threshold) + ".names")
dt.write2dArray(
least_similar_clusters,
"Clusters/" + filename + "LeastSimilarHIGH" + str(high_threshold) +
"," + str(low_threshold) + ".clusters")
dt.writeArrayDict(
cluster_name_dict, "Clusters/" + filename + "MostSimilarCLUSTER" +
str(high_threshold) + "," + str(low_threshold) + ".names")
def __init__(self,
epochs=1,
learn_rate=0.01,
loss="mse",
batch_size=1,
decay=1e-06,
hidden_activation="tanh",
layer_init="glorot_uniform",
output_activation="tanh",
hidden_layer_size=100,
file_name="unspecified_filename",
vector_path=None,
reg=0,
optimizer_name="rmsprop",
class_names=None,
noise=0,
output_weights=None):
# Initialize the model
self.model = Sequential()
# Import the numpy vectors
try:
movie_vectors = np.asarray(np.load(vector_path))
except OSError:
# If it fails, assume that it's in a standard format for vectors and then save it in numpy format
movie_vectors = dt.importVectors(vector_path)
movie_vectors = np.asarray(movie_vectors)
np.save(file_name, movie_vectors)
# Set the input and the output to be the same size, as this is an auto-encoder
input_size = len(movie_vectors[0])
output_size = len(movie_vectors[0])
if noise > 0: # If using a noisy autoencoder, add GaussianNoise layers to the start of the encoder
self.model.add(GaussianNoise(noise, input_shape=(input_size, )))
self.model.add(
Dense(output_dim=hidden_layer_size,
input_dim=input_size,
init=layer_init,
activation=hidden_activation,
W_regularizer=l2(reg)))
else:
# Otherwise just add the hidden layer
self.model.add(
Dense(output_dim=hidden_layer_size,
input_dim=input_size,
init=layer_init,
activation=hidden_activation,
W_regularizer=l2(reg)))
# If using custom weights on the hidden layer to the output layer, apply those custom weights. Otherwise just add output layer.
if output_weights == None:
self.model.add(
Dense(output_dim=output_size,
init=layer_init,
activation=output_activation))
else:
self.model.add(
Dense(output_dim=len(output_weights[0]),
init=layer_init,
activation=output_activation,
weights=output_weights))
# Compile the model and fit it to the data
if optimizer_name == "sgd":
optimizer = SGD(lr=learn_rate, decay=decay)
elif optimizer_name == "rmsprop":
optimizer = RMSprop(lr=learn_rate)
self.model.compile(loss=loss, optimizer=optimizer)
self.model.fit(movie_vectors,
movie_vectors,
nb_epoch=epochs,
batch_size=batch_size,
verbose=1)
# Create a truncated model that has no output layer that has the same weights as the previous model and use it to obtain the hidden layer representation
truncated_model = Sequential()
total_file_name = "newdata/spaces/" + file_name + ".mds"
truncated_model.add(GaussianNoise(noise, input_shape=(input_size, )))
truncated_model.add(
Dense(output_dim=hidden_layer_size,
input_dim=input_size,
init=layer_init,
activation=hidden_activation,
W_regularizer=l2(reg)))
truncated_model.compile(loss=loss, optimizer=optimizer)
self.end_space = truncated_model.predict(movie_vectors)
np.save(self.end_space, total_file_name)
def createTermClusters(self, hv_directions, lv_directions, hv_names,
lv_names):
least_similar_clusters = []
least_similar_cluster_ids = []
least_similar_cluster_names = []
directions_to_add = []
names_to_add = []
print "Overall amount of HV directions: ", len(hv_directions)
# Create high-valued clusters
amt_of_clusters = len(hv_directions[0]) * 2
for i in range(len(hv_directions)):
if i == 0:
least_similar_cluster_ids.append(i)
least_similar_clusters.append(hv_directions[i])
least_similar_cluster_names.append(hv_names[i])
print "Least Similar Term", hv_names[i]
elif i >= amt_of_clusters:
directions_to_add.append(hv_directions[i])
names_to_add.append(hv_names[i])
print "Added", hv_names[
i], "To the remaining directions to add"
else:
ti = getNextClusterTerm(least_similar_clusters, hv_directions,
least_similar_cluster_ids, 1)
least_similar_cluster_ids.append(ti)
least_similar_clusters.append(hv_directions[ti])
least_similar_cluster_names.append(hv_names[ti])
print str(i + 1) + "/" + str(
amt_of_clusters), "Least Similar Term", hv_names[ti]
# Add remaining high value directions to the low value direction list
directions_to_add.reverse()
names_to_add.reverse()
for i in range(len(directions_to_add)):
lv_directions.insert(0, directions_to_add[i])
lv_names.insert(0, names_to_add[i])
# Initialize dictionaries for printing / visualizing
cluster_name_dict = OrderedDict()
for c in least_similar_cluster_names:
cluster_name_dict[c] = []
# For every low value direction, find the high value direction its most similar to and append it to the directions
every_cluster_direction = []
for i in least_similar_clusters:
every_cluster_direction.append([i])
# Reversing so that the top names and directions are first
lv_names.reverse()
lv_directions.reverse()
# Finding the most similar directions to each cluster_centre
# Creating a dictionary of {cluster_centre: [cluster_direction(1), ..., cluster_direction(n)]} pairs
for d in range(len(lv_directions)):
i = getXMostSimilarIndex(lv_directions[d], least_similar_clusters,
[], 1)[0]
every_cluster_direction[i].append(lv_directions[d])
print str(d + 1) + "/" + str(
len(lv_directions)), "Most Similar to", lv_names[
d], "Is", least_similar_cluster_names[i]
cluster_name_dict[least_similar_cluster_names[i]].append(
lv_names[d])
# Mean of all directions = cluster direction
cluster_directions = []
for l in range(len(least_similar_clusters)):
cluster_directions.append(
dt.mean_of_array(every_cluster_direction[l]))
"""
# Get the 10 most similar and least similar directions to save later
most_similar = []
least_similar = []
most_similar_indexes = []
least_similar_indexes = []
indexes_to_find = []
for k in sorted(cluster_amt_dict, key=cluster_amt_dict.get, reverse=True):
name_to_get_most_similar = k
index_to_find = dt.getIndexInArray(hv_names, name_to_get_most_similar)
amt = 10
indexes_to_find.append(index_to_find)
most_similar_index = getXMostSimilarIndex(hv_directions[index_to_find], hv_directions, [index_to_find], amt)
least_similar_index = getXLeastSimilarIndex(hv_directions[index_to_find], hv_directions, [index_to_find], amt)
most_similar_indexes.append(most_similar_index)
least_similar_indexes.append(least_similar_index)
for m in range(len(most_similar_indexes)):
line_to_append = []
for v in range(len(most_similar_indexes[m])):
line_to_append.append(hv_names[most_similar_indexes[m][v]])
most_similar.append([cluster_dict_names[m][0], line_to_append])
for l in range(len(least_similar_indexes)):
line_to_append = []
for v in range(len(least_similar_indexes[l])):
line_to_append.append(hv_names[least_similar_indexes[l][v]])
least_similar.append([cluster_dict_names[l][0], line_to_append])
"""
return least_similar_cluster_names, cluster_name_dict, cluster_directions
def __init__(self, direction_fn, ppmi_fn, phrases_fn, phrases_to_check_fn,
fn):
ppmi = dt.importLabels(ppmi_fn)
ppmi = np.asarray(ppmi)
phrases = dt.importString(phrases_fn)
indexes_to_get = []
if phrases_to_check_fn != "":
phrases_to_check = dt.importString(phrases_to_check_fn)
for pc in range(len(phrases_to_check)):
for p in range(len(phrases)):
if phrases_to_check[pc] == phrases[p][6:]:
indexes_to_get.append(p)
ppmi = ppmi.transpose()
print len(ppmi), len(ppmi[0])
scores = []
pvalues = []
scores_kendall = []
pvalues_kendall = []
counter = 0
averages = []
with open(direction_fn) as f:
for line in f:
if indexes_to_get is not []:
for i in indexes_to_get:
if i == counter:
total = 0
amt = 0
direction = line.split()
for d in range(len(direction)):
direction[d] = float(direction[d])
new_direction = []
new_ppmi = []
direction_rank = np.argsort(direction)
ppmi_rank = np.argsort(ppmi[counter])
for d in range(len(ppmi[counter])):
if ppmi[counter][d] != 0:
total += ppmi[counter][d]
amt += 1
new_direction.append(direction_rank[d])
new_ppmi.append(ppmi_rank[d])
average = total / amt
rho, pvalue = spearmanr(new_ppmi, new_direction)
scores.append(rho)
pvalues.append(pvalue)
scores_kendall.append(rhok)
pvalues_kendall.append(pvaluek)
averages.append(average)
print phrases[counter] + ":", rho, pvalue, average
else:
direction = line.split()
for d in range(len(direction)):
direction[d] = float(direction[d])
direction_rank = np.argsort(direction)
ppmi_rank = np.argsort(ppmi[counter])
rho, pvalue = spearmanr(direction_rank, ppmi_rank)
scores.append(rho)
pvalues.append(pvalue)
print phrases[counter] + ":", rho, pvalue
counter += 1
dt.write1dArray(scores, "RuleType/s" + fn + ".score")
dt.write1dArray(pvalues, "RuleType/s" + fn + ".pvalue")
dt.write1dArray(scores_kendall, "RuleType/k" + fn + ".score")
dt.write1dArray(pvalues_kendall, "RuleType/k" + fn + ".pvalue")
dt.write1dArray(phrases, "RuleType/" + fn + ".names")
dt.write1dArray(averages, "RuleType/" + fn + ".averages")
def __init__(self, direction_fn, ppmi_fn, phrases_fn, phrases_to_check_fn,
fn):
ppmi = dt.importLabels(ppmi_fn)
ppmi = np.asarray(ppmi)
phrases = dt.importString(phrases_fn)
indexes_to_get = []
if phrases_to_check_fn != "":
phrases_to_check = dt.importString(phrases_to_check_fn)
for pc in range(len(phrases_to_check)):
for p in range(len(phrases)):
if phrases_to_check[pc] == phrases[p][6:]:
indexes_to_get.append(p)
indexes_to_get.sort()
ppmi = ppmi.transpose()
print len(ppmi), len(ppmi[0])
scores = []
pvalues = []
scores_kendall = []
pvalues_kendall = []
agini = []
agini1 = []
angini1 = []
angini = []
amap = []
andcg = []
counter = 0
averages = []
with open(direction_fn) as f:
for line in f:
exists = True
if phrases_to_check_fn != "":
exists = False
for i in indexes_to_get:
if i == counter:
exists = True
break
if exists:
total = 0
amt = 0
direction = line.split()
for d in range(len(direction)):
direction[d] = float(direction[d])
new_direction = []
new_ppmi = []
direction_rank = np.argsort(direction)
ppmi_rank = np.argsort(ppmi[counter])
for d in range(len(ppmi[counter])):
if ppmi[counter][d] != 0:
total += ppmi[counter][d]
amt += 1
new_direction.append(direction_rank[d])
new_ppmi.append(ppmi_rank[d])
average = total / amt
min_max_scaler = preprocessing.MinMaxScaler()
normalized_ppmi = min_max_scaler.fit_transform(
ppmi[counter])
normalized_dir = min_max_scaler.fit_transform(direction)
ginis = gini(normalized_ppmi, normalized_dir)
ranked_ppmi = dt.sortByArray(new_ppmi, new_direction)
nr_ppmi = min_max_scaler.fit_transform(ranked_ppmi)
ndcgs = ndcg_at_k(nr_ppmi, len(nr_ppmi))
#binarizer = preprocessing.Binarizer()
#binary_ppmi = binarizer.transform(normalized_ppmi)
#normalized_dir = np.ndarray.tolist(normalized_dir)
map = 0 #average_precision_score(normalized_ppmi, normalized_dir)
rho, pvalue = spearmanr(new_ppmi, new_direction)
rhok, pvaluek = kendalltau(new_ppmi, new_direction)
scores.append(rho)
pvalues.append(pvalue)
scores_kendall.append(rhok)
pvalues_kendall.append(pvaluek)
andcg.append(ndcgs)
agini.append(ginis)
amap.append(map)
averages.append(average)
print phrases[counter] + ":", map, ginis
counter += 1
dt.write1dArray(scores, "RuleType/s" + fn + ".score")
dt.write1dArray(pvalues, "RuleType/s" + fn + ".pvalue")
dt.write1dArray(scores_kendall, "RuleType/k" + fn + ".score")
dt.write1dArray(pvalues_kendall, "RuleType/k" + fn + ".pvalue")
dt.write1dArray(phrases, "RuleType/" + fn + ".names")
dt.write1dArray(averages, "RuleType/" + fn + ".averages")
dt.write1dArray(agini, "RuleType/gn" + fn + ".score")
dt.write1dArray(andcg, "RuleType/ndcg" + fn + ".score")
dt.write1dArray(amap, "RuleType/map" + fn + ".score")
