import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import h2o
from h2o.estimators.gbm import H2OGradientBoostingEstimator

%matplotlib inline

h2o.init()

data_df = h2o.import_file("../input/data.csv", destination_frame="data_df")

data_df.describe()

df_group = data_df.group_by("diagnosis").count()
df_group.get_frame()

features = [f for f in data_df.columns if f not in ["id", "diagnosis", "C33"]]

i = 0
t0 = data_df[data_df["diagnosis"] == "M"].as_data_frame()
t1 = data_df[data_df["diagnosis"] == "B"].as_data_frame()

# sns.set_style('whitegrid')
plt.figure()
fig, ax = plt.subplots(6, 5, figsize=(16, 24))

for feature in features:
    i += 1
    plt.subplot(6, 5, i)
    sns.kdeplot(t0[feature], bw=0.5, label="Malignant")
    sns.kdeplot(t1[feature], bw=0.5, label="Benign")
    plt.xlabel(feature, fontsize=12)
    locs, labels = plt.xticks()
    plt.tick_params(axis="both", which="major", labelsize=12)
plt.show()

plt.figure(figsize=(16, 16))
corr = data_df[features].cor().as_data_frame()
corr.index = features
sns.heatmap(
    corr,
    annot=True,
    cmap="coolwarm",
    linecolor="white",
    vmin=-1,
    vmax=1,
    cbar_kws={"orientation": "vertical"},
)
plt.title("Correlation Heatmap", fontsize=14)
plt.show()

train_df, valid_df, test_df = data_df.split_frame(ratios=[0.6, 0.2], seed=2018)
target = "diagnosis"
train_df[target] = train_df[target].asfactor()
valid_df[target] = valid_df[target].asfactor()
test_df[target] = test_df[target].asfactor()
print(
    "학습, 검증, 테스트 데이터셋의 수 : ",
    train_df.shape[0],
    valid_df.shape[0],
    test_df.shape[0],
)

predictors = features
gbm = H2OGradientBoostingEstimator()
gbm.train(x=predictors, y=target, training_frame=train_df)

gbm.summary()

print(gbm.model_performance(valid_df))

gbm.varimp_plot()

pred_val = list(gbm.predict(test_df[predictors])[0])
true_val = list(test_df[target])
prediction_acc = np.mean(pred_val == true_val)
print("Prediction accuracy: ", prediction_acc)

import Bio

print(Bio.__doc__)

from Bio import Entrez
from Bio import SeqIO

Entrez.email = "A.N.Other@example.com"
with Entrez.efetch(
    db="nucleotide", rettype="fasta", retmode="text", id="1490011893"
) as handle:
    seq_record = SeqIO.read(handle, "fasta")

seq_record

taq = seq_record.seq
len(taq)

from Bio.SeqUtils import GC

GC(taq)

taq.upper()

taq.lower()

taq.transcribe()

taq.translate()

from Bio.SeqUtils import molecular_weight

molecular_weight(taq)

molecular_weight(taq.translate())

from Bio.Data import CodonTable

print(CodonTable.unambiguous_dna_by_name["Standard"])

from Bio.SeqUtils import MeltingTemp
from Bio.Seq import Seq

primer_DNA = Seq("ATGGAGGAGATGCTGCCCCTCT")
MeltingTemp.Tm_Wallace(primer_DNA)

from Bio.SeqUtils import seq1, seq3

seq3("MEEMLPLFEPKGRVLLVDGHHLAYRTFHALKGLTTSRGEPVQAVYGFAKSLLKA")

seq1(
    "MetGluGluMetLeuProLeuPheGluProLysGlyArgValLeuLeuValAspGlyHisHisLeuAlaTyrArgThrPheHisAlaLeuLysGlyLeuThrThrSerArgGlyGluProValGlnAlaValTyrGlyPheAlaLysSerLeuLeuLysAla"
)

from Bio import AlignIO
from Bio.motifs import Motif
from Bio import motifs
from Bio.Seq import Seq
from Bio.Alphabet import IUPAC
from IPython.display import Image

alignment = AlignIO.read("./data/HBA.aln", "clustal")
instance = []

for record in alignment:
    s = Seq(str(record.seq), IUPAC.protein)
    instance.append(s)

m = motifs.create(instance)
Motif.weblogo(m, "HBA_logo.png")
Image("HBA_logo.png")

%matplotlib inline
from Bio import Phylo

tree = Phylo.read("./data/HBA.newick", "newick")
Phylo.draw(tree)

from Bio import Entrez

Entrez.email = "your@email.com"
handle = Entrez.esearch(db="pubmed", term="machine learning")
record = Entrez.read(handle)
print("Pubmed에 machine learning를 검색하면 총 {}개의 결과".format(record["Count"]))

from Bio.KEGG import REST

human_pathways = REST.kegg_list("pathway", "hsa").read()

pathways = []
for line in human_pathways.rstrip().split("\n"):
    entry, description = line.split("\t")
    if "repair" in description.lower():
        pathways.append(entry)
        print(entry, description)
print(pathways)

genes = []
for pathway in pathways:
    pathway_file = REST.kegg_get(pathway).read()
    current_section = None
    for line in pathway_file.rstrip().split("\n"):
        section = line[:12].strip()
        if not section == "":
            current_section = section
            if current_section == "GENE":
                gene_identifiers, gene_description = line[12:].split("; ")
                gene_id, gene_symbol = gene_identifiers.split()
                if gene_symbol not in genes:
                    genes.append(gene_symbol)
print("pathway에 연관된 유전자는 {} 이다".format(",".join(genes)))