def Generate_features_scenario5(Subsampled_data_frame,playlist_info,playlists,track_info_dict,list_to_try,CV_list_of_common_tracks,CV_list_of_common_albums,CV_list_of_common_artists,list_of_vectorisers,Training):
proportions = Get_Proportion(Subsampled_data_frame["TO_PREDICT"],playlists)
featuredaf = pd.DataFrame({"PROPORTIONS":proportions})
Extract_Special_Chars_From_Track_NAME(featuredaf,[str(track_info_dict[ids]['track_name']) for ids in Subsampled_data_frame["TO_PREDICT"]],Subsampled_data_frame["PID"])
Extract_Shared_Final_Artist_Album(featuredaf,Subsampled_data_frame,track_info_dict)
featuredaf["num_tracks"] = np.array([playlist_info["num_tracks"][ii] for ii in Subsampled_data_frame["PID"]])
featuredaf["Song_duration"] = np.array([track_info_dict[ids]['duration_ms'] for ids in Subsampled_data_frame["TO_PREDICT"]])
featuredaf["Song_duration_relative"] = (featuredaf["Song_duration"] - featuredaf["track_mean"])/np.sqrt(featuredaf["track_variance"])
#shared_track_name_words = [' '.join(words) for words in shared_track_name]
#shared_album_name_words = [' '.join(words) for words in shared_album_name]
#shared_artist_name_words = [' '.join(words) for words in shared_artist_name]
SPARSE_ARTIST_ALBUM_TRACK_ARRAY = Artist_Album_Track_SPARSE(Subsampled_data_frame,track_info_dict,list_to_try,CV_list_of_common_tracks,CV_list_of_common_albums,CV_list_of_common_artists)
if Training:
Huge_BOW, vectorizers = BOW(Subsampled_data_frame,playlist_info,playlists,track_info_dict,Training,list_of_vectorisers,5)
else:
Huge_BOW = BOW(Subsampled_data_frame,playlist_info,playlists,track_info_dict,Training,list_of_vectorisers,5)
output = hstack((featuredaf,Huge_BOW,SPARSE_ARTIST_ALBUM_TRACK_ARRAY)).tocsr()
if Training:
return(output,vectorizers)
else:
return(output)
def Generate_features_scenario9(Subsampled_data_frame, playlist_info,
playlists, track_info_dict, list_to_try,
CV_list_of_common_tracks,
CV_list_of_common_albums,
CV_list_of_common_artists, list_of_vectorisers,
Training):
proportions = Get_Proportion(Subsampled_data_frame["TO_PREDICT"],
playlists)
featuredaf = pd.DataFrame({"PROPORTIONS": proportions})
Extract_Special_Chars_From_Playlist_NAME(featuredaf, playlist_info["name"],
Subsampled_data_frame["PID"])
Extract_Special_Chars_From_Track_NAME(featuredaf, [
str(track_info_dict[ids]['track_name'])
for ids in Subsampled_data_frame["TO_PREDICT"]
], Subsampled_data_frame["PID"])
Extract_Shared_Artist_Album(featuredaf, Subsampled_data_frame,
track_info_dict)
shared_track_name = Find_Common_Words([
track_info_dict[ids]['track_name']
for ids in Subsampled_data_frame["TO_PREDICT"]
], [playlist_info["name"][ii] for ii in Subsampled_data_frame["PID"]])
shared_album_name = Find_Common_Words([
track_info_dict[ids]['album_name']
for ids in Subsampled_data_frame["TO_PREDICT"]
], [playlist_info["name"][ii] for ii in Subsampled_data_frame["PID"]])
shared_artist_name = Find_Common_Words([
track_info_dict[ids]['artist_name']
for ids in Subsampled_data_frame["TO_PREDICT"]
], [playlist_info["name"][ii] for ii in Subsampled_data_frame["PID"]])
featuredaf["shared_track_num"] = np.array(list(map(len,
shared_track_name)))
featuredaf["shared_album_num"] = np.array(list(map(len,
shared_album_name)))
featuredaf["shared_artist_num"] = np.array(
list(map(len, shared_artist_name)))
featuredaf["num_tracks"] = np.array([
playlist_info["num_tracks"][ii] for ii in Subsampled_data_frame["PID"]
])
featuredaf["Song_duration"] = np.array([
track_info_dict[ids]['duration_ms']
for ids in Subsampled_data_frame["TO_PREDICT"]
])
featuredaf["Song_duration_relative"] = (
featuredaf["Song_duration"] - featuredaf["track_mean"]) / np.sqrt(
featuredaf["track_variance"])
#shared_track_name_words = [' '.join(words) for words in shared_track_name]
#shared_album_name_words = [' '.join(words) for words in shared_album_name]
#shared_artist_name_words = [' '.join(words) for words in shared_artist_name]
tv11 = list_of_vectorisers[10]
playlist_names = cleaner(
[playlist_info["name"][ii] for ii in Subsampled_data_frame["PID"]])
SPARSE_ARTIST_ALBUM_TRACK_ARRAY = Artist_Album_Track_SPARSE(
Subsampled_data_frame, track_info_dict, list_to_try,
CV_list_of_common_tracks, CV_list_of_common_albums,
CV_list_of_common_artists)
if Training:
Huge_BOW, vectorizers = BOW(Subsampled_data_frame, playlist_info,
playlists, track_info_dict, Training,
list_of_vectorisers, 9)
CHAR_GRAM = tv11.fit_transform(playlist_names)
vectorizers.append(tv11)
else:
Huge_BOW = BOW(Subsampled_data_frame, playlist_info, playlists,
track_info_dict, Training, list_of_vectorisers, 9)
CHAR_GRAM = tv11.transform(playlist_names)
output = hstack((featuredaf, CHAR_GRAM, SPARSE_ARTIST_ALBUM_TRACK_ARRAY,
Huge_BOW)).tocsr()
if Training:
return (output, vectorizers)
else:
return (output)
def Generate_features_scenario1(Subsampled_data_frame, playlist_info,
playlists, track_info_dict,
list_of_vectorisers, Training):
proportions = Get_Proportion(Subsampled_data_frame["TO_PREDICT"],
playlists)
featuredaf = pd.DataFrame({"PROPORTIONS": proportions})
Extract_Special_Chars_From_Playlist_NAME(featuredaf, playlist_info["name"],
Subsampled_data_frame["PID"])
Extract_Special_Chars_From_Track_NAME(featuredaf, [
str(track_info_dict[ids]['track_name'])
for ids in Subsampled_data_frame["TO_PREDICT"]
], Subsampled_data_frame["PID"])
shared_track_name = Find_Common_Words([
track_info_dict[ids]['track_name']
for ids in Subsampled_data_frame["TO_PREDICT"]
], [playlist_info["name"][ii] for ii in Subsampled_data_frame["PID"]])
shared_album_name = Find_Common_Words([
track_info_dict[ids]['album_name']
for ids in Subsampled_data_frame["TO_PREDICT"]
], [playlist_info["name"][ii] for ii in Subsampled_data_frame["PID"]])
shared_artist_name = Find_Common_Words([
track_info_dict[ids]['artist_name']
for ids in Subsampled_data_frame["TO_PREDICT"]
], [playlist_info["name"][ii] for ii in Subsampled_data_frame["PID"]])
featuredaf["shared_track_num"] = np.array(list(map(len,
shared_track_name)))
featuredaf["shared_album_num"] = np.array(list(map(len,
shared_album_name)))
featuredaf["shared_artist_num"] = np.array(
list(map(len, shared_artist_name)))
featuredaf["num_tracks"] = np.array([
playlist_info["num_tracks"][ii] for ii in Subsampled_data_frame["PID"]
])
featuredaf["Song_duration"] = np.array([
track_info_dict[ids]['duration_ms']
for ids in Subsampled_data_frame["TO_PREDICT"]
])
playlist_names = cleaner(
[playlist_info["name"][ii] for ii in Subsampled_data_frame["PID"]])
tv11 = list_of_vectorisers[10]
if Training:
CHAR_GRAM = tv11.fit_transform(playlist_names)
Huge_BOW, vectorizers = BOW(Subsampled_data_frame, playlist_info,
playlists, track_info_dict, Training,
list_of_vectorisers, 1)
vectorizers.append(tv11)
else:
Huge_BOW = BOW(Subsampled_data_frame, playlist_info, playlists,
track_info_dict, Training, list_of_vectorisers, 1)
CHAR_GRAM = tv11.transform(playlist_names)
output = hstack((featuredaf, Huge_BOW, CHAR_GRAM)).tocsr()
if Training:
return (output, vectorizers)
else:
return (output)