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# file to edit: dev_nb/BacteriaClassifier.ipynb

import sys

sys.path.append("..")

from faigen.data import sequence

from faigen.data.sequence import regex_filter, count_filter, Dna2VecDataBunch

from functools import partial

import pandas as pd

import numpy as np

from sklearn.decomposition import PCA

from sklearn import manifold,neighbors

from scipy.cluster.hierarchy import dendrogram, linkage

from matplotlib import pyplot as plt

import seaborn as sns; sns.set(color_codes=True)

import plotly.plotly as py

import plotly.graph_objs as go

from fastai import *

from fastai.data_block import *

from fastai.basic_train import *

from fastai.layers import *

from fastai.metrics import *

from gensim.models import Word2Vec

import torch

import torch.nn as nn

import torch.nn.functional as F

if __name__=='__main__':

    print("Loading embedding")

    word_vectors = Word2Vec.load_word2vec_format('../faigen/pretrained/embeddings/dna2vec-20190611-1940-k8to8-100d-10c-4870Mbp-sliding-LmP.w2v')

    print("Loading Data")

    DB="/data/genomes/GenSeq_fastas"

    # DB='/home/serge/development/genomes/ncbi-genomes-2019-04-07/bacterial genomes'

    filters=[partial(regex_filter, rx="Bacillus|Staphylococcus|Vibrio|Rhizobium"),partial(regex_filter, rx="plasmid?\s", keep=False)]

    #        partial(count_filter,num_fastas=(1,1), keep=1)]

    bunch = Dna2VecDataBunch.from_folder(DB,test="test",

                 filters=filters,

                 labeler=lambda x: x.split()[1],

                 emb=word_vectors,ngram=8,skip=0,

                 n_cpus=7,agg=partial(np.mean, axis=0))

    print("Creating Learner")

    layers=[nn.Linear(bunch.train_dl.x.c,10),nn.ReLU(),

            nn.Linear(10,bunch.train_dl.y.c)]

    bac_classifier = SequentialEx(*layers)

    print(bac_classifier)

    learn = Learner(bunch, bac_classifier, metrics=[accuracy])

    print ("Training")

    learn.fit_one_cycle(3,5e-2)