def graph_features(self):
if not self.graph_features:
try:
self.graph_features = SFrame.read_csv(
f"{DATA_PATH}/bechdel_features.csv")
except:
t = triangles()
self.graph_features = SFrame.read_csv(
"../temp/graph_features.csv")
self.graph_features = self.graph_features.join(
SFrame(get_female_in_top_10_roles()),
on={
"movie_name": "movie_name",
"year": "year"
})
self.graph_features = self.graph_features.join(
SFrame(t), on={
"movie_name": "movie",
"year": "year"
})
self.graph_features["total_tri"] = self.graph_features["0"] + self.graph_features["1"] + \
self.graph_features["2"] + self.graph_features["3"]
for i in range(4):
self.graph_features[f"{i}%"] = self.graph_features[str(
i)] / self.graph_features["total_tri"]
self.graph_features.save(f"{DATA_PATH}/bechdel_features.csv",
"csv")
return self.graph_features
def _get_sframes(features_train, features_test, labels_train, labels_test):
logging.debug(f"turi._get_sframes()")
train_data: pandas.DataFrame = features_train.join(labels_train)
test_data: pandas.DataFrame = features_test.join(labels_test)
train_data_sf = SFrame(data=train_data)
test_data_sf = SFrame(data=test_data)
return train_data_sf, test_data_sf
def similor_sort(sourceData, classicData, num):
"""
:param sourceData: dataframe include
:param classicData: classic picture
:param num: how many picture to pick out
:return:
"""
start_time = time.time()
ref_data = SFrame()
for index, row in sourceData.iterrows():
#print row
path = row['path']
img = tc.Image(path)
ref_data = ref_data.append(SFrame({'path': [path], 'image': [img]}))
ref_data = ref_data.add_row_number()
# print ref_data
query_data = SFrame()
for index, row in classicData.iterrows():
path = row['path']
img = tc.Image(path)
query_data = query_data.append(SFrame({
'path': [path],
'image': [img]
}))
query_data = query_data.add_row_number()
model = tc.image_similarity.create(ref_data,
label=None,
feature=None,
model='resnet-50',
verbose=True)
if num == 0:
num = ref_data.num_rows()
similar_images = model.query(query_data, k=num)
ret_array = np.zeros((query_data.num_rows(), num))
for image in similar_images:
ref_label = image['reference_label']
distance = image['distance']
query_label = image['query_label']
ret_array[query_label][ref_label] = distance
mean = np.mean(ret_array, axis=0)
sourceData.insert(2, 'distance', (mean))
#sort = np.argsort(mean)
# print sourceData
elapsed_time = time.time() - start_time
print("Time elapsed = %d" % (elapsed_time))
return sourceData
def __init__(self):
self.imgframe = tc.load_sframe('model/final/final.sframe')
self.model = tc.load_model('model/final/final_model')
self.sample = tc.Image()
self.results = SFrame()
self.rows = SArray()
self.pathlist = []
self.distance_list = []
def urls(self):
"""
Creating URLs SFrame from.txt.gz files
"""
cols = ["PaperId", "SourceType", "SourceUrl", "LanguageCode"]
urls = SFrame(
pd.read_csv(self._dataset_dir / "PaperUrls.txt.gz",
sep="\t",
names=cols).replace({pd.NA: None}))
return urls.groupby("PaperId", {"Urls": agg.CONCAT("SourceUrl")})
def __init__(self):
self.bechdel = SFrame.read_csv(f"{DATA_PATH}/bechdel.csv",
column_type_hints={"imdbid": str})
self.bechdel.sort("year", False)
self.bechdel["tconst"] = "tt" + self.bechdel["imdbid"]
self.bechdel_imdb = imdb_data.title.join(self.bechdel)
self.clf = RandomForestClassifier(n_jobs=-1,
n_estimators=100,
max_depth=5,
random_state=1)
self._graph_features = SFrame()
def fields_of_study_papers_ids(self, levels=(1, 2, 3)):
"""
Creates SFrames with each Fields of study PaperIds
:param levels: list of fields of study level
"""
sf = SFrame()
for level in tqdm(levels):
sf = sf.append(self._create_field_of_study_paper_ids(level))
return sf
def fields_of_study(self):
"""
Creating Field of study SFrame from.txt.gz files
"""
cols = [
"FieldOfStudyId", "Rank", "NormalizedName", "DisplayName",
"MainType", "Level", "PaperCount", "CitationCount", "CreatedDate"
]
fields_of_study = SFrame(
pd.read_csv(self._dataset_dir / "FieldsOfStudy.txt.gz",
sep="\t",
names=cols).replace({pd.NA: None}))
return fields_of_study
def paper_resources(self):
"""
Creating Field of study SFrame from.txt.gz files
ResourceType. 1 = Project, 2 = Data, 4 = Code
"""
cols = [
"PaperId", "ResourceType", "ResourceUrl", "SourceUrl",
"RelationshipType"
]
return SFrame(
pd.read_csv(self._dataset_dir / "PaperResources.txt.gz",
sep="\t",
names=cols).replace({pd.NA: None}))
def journals(self):
"""
Create the Papers SFrame object from.txt.gz files which contains information on each paper
"""
cols = [
"JournalId", "Rank", "NormalizedName", "DisplayName", "Issn",
"Publisher", "Webpage", "PaperCount", "CitationCount",
"CreatedDate"
]
journals = SFrame(
pd.read_csv(self._dataset_dir / "Journals.txt.gz",
sep="\t",
names=cols).replace({pd.NA: None}))
return journals
def get_user_preferences(user_id, self):
pref_df = self.get_data_frame('pref')
comb_df = self.get_data_frame('comb')
ratings_frame = SFrame(pd.merge(pref_df, comb_df, on='combination_id'))
item_sim_model = item_similarity_recommender.create(
ratings_frame,
user_id='user_id',
item_id='combination_id',
target='rating',
similarity_type='cosine')
return item_sim_model.recommend(users=[].append(user_id),
k=10).to_dataframe().values
def paper_author_affiliations(self):
"""
Creating authors affiliation SFrame from.txt.gz files
:return:
"""
cols = [
"PaperId", "AuthorId", "AffiliationId", "AuthorSequenceNumber",
"OriginalAuthor", "OriginalAffiliation"
]
paper_author_affiliations = SFrame(
pd.read_csv(self._dataset_dir / "PaperAuthorAffiliations.txt.gz",
sep="\t",
names=cols).replace({pd.NA: None}))
return paper_author_affiliations
def affiliations(self):
"""
Creating authors affiliation SFrame from.txt.gz files
:return:
"""
cols = [
"AffiliationId", "Rank", "NormalizedName", "DisplayName", "GridId",
"OfficialPage", "WikiPage", "PaperCount", "CitationCount",
"CreatedDate"
]
affiliations = SFrame(
pd.read_csv(self._dataset_dir / "Affiliations.txt.gz",
sep="\t",
names=cols).replace({pd.NA: None}))
return affiliations
def sjr_to_csv(self, regex):
sjr_sf = SFrame()
for p in self._dataset_dir.glob(regex):
if p.suffix == ".csv":
y = int(re.match(r'.*([1-3][0-9]{3})', p.name).group(1))
sf = SFrame.read_csv(str(p), delimiter=';')
sf['Year'] = y
sf = sf.rename({"Total Docs. (%s)" % y: "Total Docs."})
extra_cols = ["Categories"]
for c in extra_cols:
if c not in sf.column_names():
sf[c] = ''
sjr_sf = sjr_sf.append(sf)
r_issn = re.compile('(\\d{8})')
sjr_sf['Issn'] = sjr_sf['Issn'].apply(lambda i: r_issn.findall(i))
return sjr_sf.stack('Issn', new_column_name='ISSN')
def authors(self):
"""
Creates authors names SFrames from.txt.gz files
"""
authors = SFrame(
pd.read_csv(self._dataset_dir / "Authors.txt.gz",
sep="\t",
names=[
"AuthorId", "Rank", "NormalizedName",
"DisplayName", "LastKnownAffiliationId",
"PaperCount", "CitationCount", "CreatedDate"
]).replace({pd.NA: None}))
authors['First name'] = authors['NormalizedName'].apply(
lambda s: s.split()[0])
authors['Last name'] = authors['NormalizedName'].apply(
lambda s: s.split()[-1])
return authors
def sframe(frame):
sf = SFrame(frame)
sf.explore()
sf.show()
return
def _copy_from_sarray(sa, buf, start, end, field_length, bias=0):
assert isinstance(sa, SArray)
sf = SFrame({'__tmp__': sa})
_copy_from_sframe(sf, buf, start, end, [field_length], bias)
def _copy_from_sarray(sa, buf, start, end, shape, bias=0):
assert isinstance(sa, SArray)
sf = SFrame({'__tmp__': sa})
_copy_from_sframe(sf, buf, start, end, shape, bias)
if multivariate:
visualisation(y_pred=y_pred[:, 0].reshape(-1, 1),
Y_test=Y_test[:, 0].reshape(-1, 1),
y_difference=y_difference[:, 0].reshape(-1,
1)) # Insertion
visualisation(y_pred=y_pred[:, 1].reshape(-1, 1),
Y_test=Y_test[:, 1].reshape(-1, 1),
y_difference=y_difference[:, 1].reshape(-1,
1)) # Deletion
else:
visualisation(y_pred, Y_test, y_difference)
#Looking into sframe as an alternative to pandas, has s3 support
#Tensorflow also has s3 and GCP support if you install from source and enable it
sf = SFrame(build_csv(build_data(sample)[5], build_data(sample)[7]))
sf.explore()
sf.show()
else:
#Neural Network architecture
def build_regressor():
#Initialising neural network
regressor = Sequential()
#Input layer and first hidden layer with dropout
regressor.add(
Dense(units=10,
kernel_initializer='uniform',
from turicreate import item_similarity_recommender, SFrame
import pandas as pd
# import numpy as np
data_path = '/home/riffel/Projects/vardiety/vardiety-recommendation/data'
combinations = pd.read_csv(data_path + '/combinations.csv', sep=',', names=['combination_id', 'items'])
preferences = pd.read_csv(data_path + '/preferences.csv', sep=',', names=['user_id', 'combination_id', 'rating'])
ratings = pd.merge(combinations, preferences, on='combination_id')
ratings_frame = SFrame(ratings)
print(ratings_frame)
item_sim_model = item_similarity_recommender.create(
ratings_frame,
user_id='user_id',
item_id='combination_id',
target='rating',
similarity_type='pearson'
)
item_sim_recom = item_sim_model.recommend(users=[2], k=10)
print(item_sim_recom)
'''
n_users = ratings.user_id.unique().shape[0]
n_combinations = ratings.combination_id.unique().shape[0]
coordinate = {
'coordinates':
coordinates_from_bounding_box(bounding_box, image_size),
'label':
class_name,
'type':
'rectangle'
}
sframe_entry.append(coordinate)
return sframe_entry
sframe_annotations = map(
lambda kv: sframe_annotations_from_labelbox_annotations(kv),
labelbox_annotations)
# we need to flatten it to a list of dictionaries ... turicreate supports
flat_sframe_annotations = [
item for sublist in sframe_annotations for item in sublist
]
return (img, flat_sframe_annotations)
rows = map(lambda annotated_image: row_from_annotated_image(annotated_image),
total_annotated_images)
images, annotations = zip(*rows)
sframe = SFrame({"image": images, "annotations": annotations})
sframe.save(sframe_path)
print(repr(sframe))
def first_name_gender(self):
if self._first_name_gender is None:
self._first_name_gender = SFrame(f"{DATA_PATH}/first_names_gender.sframe")
self._first_name_gender = self._first_name_gender.unstack(["First Name", "Gender Dict"])[0][
"Dict of First Name_Gender Dict"]
return self._first_name_gender
sub_df = df[df.groupby('user').user.transform('count') > 20].copy()
#Getting a list of all unique users
users = sub_df['user'].unique()
# Splitting the users into train and test
users_train, users_test = train_test_split(users,
test_size=0.02,
random_state=42)
# Creating seperate dataframes for training users and testing users
train_df = sub_df[sub_df.user.isin(users_train)]
test_df = sub_df[sub_df.user.isin(users_test)]
test_df.columns = ['user', 'item', 'rating']
sf1 = SFrame(data=train_df)
sf2 = SFrame(data=test_df)
# Retaining some portion of the test users' data into training data for the Original RecSys
train, test = tc.recommender.util.random_split_by_user(sf2,
user_id='user',
item_id='item',
max_num_users=26788)
# Some portion of test user's data is added to the Original RecSys so that we can calculate their RMSE
final_sf = sf1 + train
test_users_train = train.to_dataframe()
test_users_eval = test.to_dataframe()
sample_of_users = test['user']
