--- a
+++ b/exp/nb_BacteriaClassifier.py
@@ -0,0 +1,57 @@
+
+#################################################
+### THIS FILE WAS AUTOGENERATED! DO NOT EDIT! ###
+#################################################
+# 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)
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