from sklearn import datasets
from sklearn.neural_network import MLPClassifier
import traceback
from submissions.Blue import music


class DataFrame:
    data = []
    feature_names = []
    target = []
    target_names = []

musicATRB = DataFrame()
musicATRB.data = []
targetData = []
'''
Extract data from the CORGIS Music Library.

Most 'hit' songs average 48-52 bars and no more than ~3 minutes (180 seconds)...
'''

allSongs = music.get_songs()
for song in allSongs:
    try:
        length = float(song['song']["duration"])
        targetData.append(length)

        genre = song['artist']['terms'] #String
        title = song['song']['title'] #String
        # release = float(song['song']['Release'])

        musicATRB.data.append([genre, title])

    except:
        traceback.print_exc()

musicATRB.feature_names = [
    'Genre',
    'Title',
    'Release',
    'Length',
]

musicATRB.target = []

def musicTarget(release):
    if (song['song']['duration'] <= 210
        ): #if the song is less that 3.5 minutes (210 seconds) long
        return 1
    return 0

for i in targetData:
    tt = musicTarget(i)
    musicATRB.target.append(tt)

musicATRB.target_names = [
    'Not a hit song',
    'Could be a hit song',
]

Examples = {
    'Music': musicATRB,
}

'''
Make a customn classifier,
'''
mlpc = MLPClassifier(
    hidden_layer_sizes = (100,),
    activation = 'relu',
    solver='sgd', # 'adam',
    alpha = 0.0001,
    # batch_size='auto',
    learning_rate = 'adaptive', # 'constant',
    # power_t = 0.5,
    max_iter = 1000, # 200,
    shuffle = True,
    # random_state = None,
    # tol = 1e-4,
    # verbose = False,
    # warm_start = False,
    # momentum = 0.9,
    # nesterovs_momentum = True,
    # early_stopping = False,
    # validation_fraction = 0.1,
    # beta_1 = 0.9,
    # beta_2 = 0.999,
    # epsilon = 1e-8,
)

'''
Try scaling the data.
'''
musicScaled = DataFrame()

def setupScales(grid):
    global min, max
    min = list(grid[0])
    max = list(grid[0])
    for row in range(1, len(grid)):
        for col in range(len(grid[row])):
            cell = grid[row][col]
            if cell < min[col]:
                min[col] = cell
            if cell > max[col]:
                max[col] = cell

def scaleGrid(grid):
    newGrid = []
    for row in range(len(grid)):
        newRow = []
        for col in range(len(grid[row])):
            try:
                cell = grid[row][col]
                scaled = (cell - min[col]) \
                         / (max[col] - min[col])
                newRow.append(scaled)
            except:
                pass
        newGrid.append(newRow)
    return newGrid

setupScales(musicATRB.data)
musicScaled.data = scaleGrid(musicATRB.data)
musicScaled.feature_names = musicATRB.feature_names
musicScaled.target = musicATRB.target
musicScaled.target_names = musicATRB.target_names

Examples = {
    'musicDefault': {
        'frame': musicATRB,
    },
    'MusicSGD': {
        'frame': musicATRB,
        'mlpc': mlpc
    },
    'MusisScaled': {
        'frame': musicScaled,
    },
}
from sklearn import datasets
from sklearn.neural_network import MLPClassifier
import traceback
from submissions.Blue import music


class DataFrame:
    data = []
    feature_names = []
    target = []
    target_names = []

musicATRB = DataFrame()
musicATRB.data = []
targetData = []
'''
Extract data from the CORGIS Music Library.

Most 'hit' songs average 48-52 bars and no more than ~3 minutes (180 seconds)...
'''

allSongs = music.get_songs()
for song in allSongs:
    try:
        length = float(song['song']["duration"])
        targetData.append(length)

        genre = song['artist']['terms'] #String
        title = song['song']['title'] #String
        # release = float(song['song']['Release'])

        musicATRB.data.append([genre, title])

    except:
        traceback.print_exc()

musicATRB.feature_names = [
    'Genre',
    'Title',
    'Release',
    'Length',
]

musicATRB.target = []

def musicTarget(release):
    if (song['song']['duration'] <= 210
        ): #if the song is less that 3.5 minutes (210 seconds) long
        return 1
    return 0

for i in targetData:
    tt = musicTarget(i)
    musicATRB.target.append(tt)

musicATRB.target_names = [
    'Not a hit song',
    'Could be a hit song',
]

Examples = {
    'Music': musicATRB,
}

'''
Make a customn classifier,
'''
mlpc = MLPClassifier(
    hidden_layer_sizes = (100,),
    activation = 'relu',
    solver='sgd', # 'adam',
    alpha = 0.0001,
    # batch_size='auto',
    learning_rate = 'adaptive', # 'constant',
    # power_t = 0.5,
    max_iter = 1000, # 200,
    shuffle = True,
    # random_state = None,
    # tol = 1e-4,
    # verbose = False,
    # warm_start = False,
    # momentum = 0.9,
    # nesterovs_momentum = True,
    # early_stopping = False,
    # validation_fraction = 0.1,
    # beta_1 = 0.9,
    # beta_2 = 0.999,
    # epsilon = 1e-8,
)

'''
Try scaling the data.
'''
musicScaled = DataFrame()

def setupScales(grid):
    global min, max
    min = list(grid[0])
    max = list(grid[0])
    for row in range(1, len(grid)):
        for col in range(len(grid[row])):
            cell = grid[row][col]
            if cell < min[col]:
                min[col] = cell
            if cell > max[col]:
                max[col] = cell

def scaleGrid(grid):
    newGrid = []
    for row in range(len(grid)):
        newRow = []
        for col in range(len(grid[row])):
            try:
                cell = grid[row][col]
                scaled = (cell - min[col]) \
                         / (max[col] - min[col])
                newRow.append(scaled)
            except:
                pass
        newGrid.append(newRow)
    return newGrid

setupScales(musicATRB.data)
musicScaled.data = scaleGrid(musicATRB.data)
musicScaled.feature_names = musicATRB.feature_names
musicScaled.target = musicATRB.target
musicScaled.target_names = musicATRB.target_names

Examples = {
    'musicDefault': {
        'frame': musicATRB,
    },
    'MusicSGD': {
        'frame': musicATRB,
        'mlpc': mlpc
    },
    'MusisScaled': {
        'frame': musicScaled,
    },
}
Extract data from the CORGIS Music Library.  Most 'hit' songs average 48-52 bars and no more than ~3 minutes (180 seconds)... #String #String # release = float(song['song']['Release']) #if the song is less that 3.5 minutes (210 seconds) long Make a customn classifier, # 'adam', # batch_size='auto', # 'constant', # power_t = 0.5, # 200, # random_state = None, # tol = 1e-4, # verbose = False, # warm_start = False, # momentum = 0.9, # nesterovs_momentum = True, # early_stopping = False, # validation_fraction = 0.1, # beta_1 = 0.9, # beta_2 = 0.999, # epsilon = 1e-8, Try scaling the data.