def __init__(self, pois, level=None):
"""
:param pois: dataframe with POIs and respective coordinates.
:param level: level of the category we want (e.g. Shops & Services:Gas Stations -> level 0: Shops & Services)
This param has to be used when it is necessary produce a clean file text for word2vec.
"""
self._pois = pois
self._level = level
if self._level is not None:
self._pois['categories'] = utils.select_category(
list(self._pois['categories']), level)
def from_csv(cls, input, sep='\t', category_column='categories', level=5):
# load foursquare dataset mapped on a particular grid
df = pd.read_csv(input, sep=sep)
df[category_column] = df[category_column].astype(str)
# assign category to each record of the dataset
df.loc[:, "category"] = utils.select_category(list(df[category_column]), level)
# drop entry with empty category
df = df.loc[df["category"] != "nan"]
return cls(df)
def cell_vector_representation(poi_grid, w2v_model, level, output_file, size):
"""
Takes as input a spatial grid with POIs for each cell, a Word2Vec model, and a level of detail
For each cell:
Looks up each category in a cell for the given level in the W2V model, taking the corresponding vector representation
Sums all the vectors
Returns a dataframe with a w2v representation for all words in that cell in every row
"""
# load shapefile of mapped POIs
gdf = csv_2_geodf(poi_grid)
# load w2v_model
model = gensim.models.Word2Vec.load(w2v_model)
# group every cell
grouped_gdf = gdf.groupby('cellID')
output = {}
with open(output_file, 'w') as out:
for cell, group in grouped_gdf:
output[cell] = []
for categories_raw in group['categories']:
# select level
category = utils.select_category(categories_raw.split(':'),
level)[-1]
# lookup category in w2v
try:
vector = model[category]
output[cell].append(np.array(vector))
except (KeyError):
pass
if len(output[cell]) == 0:
output[cell] = [np.zeros(int(size))]
# sum vectors
sum_w = sum_vectors(output[cell])
sum_w_str = str("\t".join(map(str, sum_w)))
text_to_write = str(cell) + '\t' + sum_w_str + '\n'
out.write(text_to_write)
def generate(self, model, area_based=True, strategy='avg'):
"""
TODO: create the NOT area based function
"""
# load w2v_model
model = gensim.models.Word2Vec.load(model)
# group every cell
grouped_gdf = gdf.groupby('cellID')
output = {}
with open(output_file, 'w') as out:
for cell, group in grouped_gdf:
output[cell] = []
for categories_raw in group['categories']:
# select level
category = utils.select_category(
categories_raw.split(':'), level)[-1]
# lookup category in w2v
try:
vector = model[category]
output[cell].append(np.array(vector))
except(KeyError):
pass
if len(output[cell]) == 0:
output[cell] = [np.zeros(int(size))]
# sum vectors
sum_w = sum_vectors(output[cell])
sum_w_str = str("\t".join(map(str, sum_w)))
text_to_write = str(cell) + '\t' + sum_w_str + '\n'
out.write(text_to_write)