def cluster_users_by_interactions_count(clip):
"""
Split the playlists based on interactions count above or below the specified clip value.
Parameters
----------
clip : (int), clip value for splitting. The playlists will be splitted in 2 groups:
those that have an interactions count <= clip and those that have an interactions count > clip.
Returns
-------
2 lists of playlists ids
"""
playlists = data.get_playlists_df()
target_playlist = pd.DataFrame({'playlist_id':data.get_target_playlists()})
target_playlist['index'] = target_playlist.index
counts = target_playlist.merge(playlists).groupby(['playlist_id', 'index']).size().reset_index(name='counts')
#counts = counts.reset_index()
#counts.columns[2] = 'index'
#counts['index'] = counts.index
# build dataframe of number of interactions: playlist_id | tracks_count
#counts = playlists.groupby('playlist_id').size().reset_index(name='counts')
# split based on the interactions counts
return counts[counts['counts']<=clip]['index'].values, counts[counts['counts']>clip]['index'].values
def histogram_of_interactions():
"""
Plot the histogram of the interactions counts:
x axis: interactions
y axis: count of playlists with that number of interactions
"""
playlists = data.get_playlists_df()
target_playlist = pd.DataFrame(
{'playlist_id': data.get_target_playlists()})
counts = playlists.merge(target_playlist).groupby(
'playlist_id').size().reset_index(name='interactions')
# plot counts for each playlist
#counts.plot(x='playlist_id', y='interactions', kind='scatter', figsize=(200,100))
hist = counts.groupby('interactions').size().reset_index(name='counts')
hist.plot(x='interactions',
y='counts',
kind='bar',
fontsize=7,
figsize=(150, 100))
# plot histogram
plt.show(block=True)
def histogram_of_top_pop_items(top_n, only_target=True):
playlists_df = data.get_playlists_df()
if only_target:
# filter only target playlist
target_playlist_df = pd.DataFrame({'playlist_id' : data.get_target_playlists()})
playlists_df = playlists_df.merge(target_playlist_df)
# track_id | count
toptracks_df = playlists_df.groupby('track_id').size().reset_index(name='count')
toptracks_df = toptracks_df.sort_values('count', ascending=False).head(top_n)
toptracks_df.plot(x='track_id', y='count', kind='bar', fontsize=6, figsize=(150,100))
# plot histogram
plt.show(block=True)
def cluster_users_by_top_pop_count(clip_perc, top_n=100, only_target=True):
"""
Return the ids of the playlists containing at least the specified percentage of top
popular track (in descending order based on contained top pop tracks count)
Parameters
----------
clip_perc: (float) returns only playlist with a percentage of top pop tracks over the total
tracks count >= clip_perc
top_n: consider only the most popular tracks (it should be set equal to the max
track count among all playlists)
only_target: (bool) consider only the target playlist
Returns
-------
List of playlist_id
"""
playlists_df = data.get_playlists_df()
#tot_interactions = playlists_df.shape[0]
if only_target:
# filter only target playlist
target_playlist_df = pd.DataFrame({'playlist_id' : data.get_target_playlists()})
playlists_df = playlists_df.merge(target_playlist_df)
# track_id | count
toptracks_df = playlists_df.groupby('track_id').size().reset_index(name='count')
#toptracks_df['relative_count'] = toptracks_df['count'] / tot_interactions
toptracks_df = toptracks_df.sort_values('count', ascending=False).head(top_n)
# playlist_id | top_pop_count
filtered_df = playlists_df.merge(toptracks_df)
filtered_df = filtered_df.groupby('playlist_id').size().reset_index(name='top_pop_count')
#filtered_df = filtered_df.sort_values('top_pop_count', ascending=False)
# playlist_id | count | top_pop_count | perc
playlists_count_df = playlists_df.groupby('playlist_id').size().reset_index(name='count')
final_df = playlists_count_df.merge(filtered_df)
final_df['perc'] = np.divide(final_df['top_pop_count'], final_df['count'])
# filter only playlist with top pop perc >= clip_perc
final_df = final_df[final_df['perc']>=clip_perc]
final_df.sort_values(['perc','top_pop_count'], ascending=False, inplace=True)
return final_df['playlist_id'].values
def cluster_users_by_interactions_count(clip):
"""
Split the playlists based on interactions count above or below the specified clip value.
Parameters
----------
clip : (int), clip value for splitting. The playlists will be splitted in 2 groups:
those that have an interactions count <= clip and those that have an interactions count > clip.
Returns
-------
2 lists of playlists ids
"""
playlists = data.get_playlists_df()
# build dataframe of number of interactions: playlist_id | tracks_count
counts = playlists.groupby('playlist_id').size().reset_index(name='counts')
# split based on the interactions counts
return counts[counts['counts'] <= clip]['playlist_id'].values, counts[
counts['counts'] > clip]['playlist_id'].values
if len(df_test[df_test['playlist_id'].isin(p)].groupby(
'playlist_id')) != len(p):
if target == 'all':
print(
"WARNING: not all the target playlists (JUST THE TARGETS) have a song in the training set"
)
elif target == 'target':
print(
"WARNING: not all the playlists (ALL OF THEM) have a song in the training set"
)
def _create_urm(df):
"""
Utility method
:param df: (panda's dataframe) represents a set of playlists and tracks (train.csv-like)
:return: (csr matrix) the urm built from the df, in format (number of playlists, number of tracks)
"""
return csr_matrix((df['rating'].values,
(df['playlist_id'].values, df['track_id'].values)),
shape=(d.N_PLAYLISTS, d.N_TRACKS))
if __name__ == "__main__":
df = d.get_playlists_df()
pi = ProcessInteractions(df)
es = ExplicateBase()
s = SplitRandom(0.2)
create_urms(pi, es, s)
elif target == 'target':
p = d.get_target_playlists()
if len(df_test[df_test['playlist_id'].isin(p)].groupby(
'playlist_id')) != len(p):
if target == 'all':
print(
"WARNING: not all the target playlists (JUST THE TARGETS) have a song in the training set"
)
elif target == 'target':
print(
"WARNING: not all the playlists (ALL OF THEM) have a song in the training set"
)
def _create_urm(df):
"""
Utility method
:param df: (panda's dataframe) represents a set of playlists and tracks (train.csv-like)
:return: (csr matrix) the urm built from the df, in format (number of playlists, number of tracks)
"""
return csr_matrix((np.ones(df.shape[0], dtype=int),
(df['playlist_id'].values, df['track_id'].values)),
shape=(d.N_PLAYLISTS, d.N_TRACKS))
df = d.get_playlists_df() # reads train.csv path
pi = ProcessInteractions(df)
s = SplitRandom(0.2)
create_urms(pi, s)
# #### Append the 'profile_length' column to the recommendation dataframe
#%%
target_ids = data.get_target_playlists()
targetURM = data.get_urm_train_1()[target_ids]
user_profile_lengths = np.array(targetURM.sum(axis=1)).flatten()
profile_lengths_df = pd.DataFrame({'playlist_id': target_ids, 'profile_length': user_profile_lengths})
#%%
rec_lengths_df = recs_df.merge(profile_lengths_df, on='playlist_id')
#%% [markdown]
# #### Popularity feature
#%%
df = data.get_playlists_df()
popularity = df.groupby(['track_id']).size().reset_index(name='popularity')
#%%
rec_pop_df = rec_lengths_df.join(popularity.set_index('track_id'), on='track_id')
#%% [markdown]
# #### Append the 'label' column
#%%
urm_test = data.get_urm_test_1()
test_labels = []
last_playlist_id = -1
for idx,row in recs_df.iterrows():
current_playlist_id = row['playlist_id']