main.py 9.15 KB
Newer Older
1 2 3 4
from flask import Flask, jsonify, request
import pandas as pd
import numpy as np
from flask_cors import CORS
5
from collections import defaultdict, Counter
6
from time import time
7 8 9
import os.path
import json
from sklearn import preprocessing
10
import orjson
11
import dask.dataframe as dd
Kruyff,D.L.W. (Dylan)'s avatar
Kruyff,D.L.W. (Dylan) committed
12 13 14 15
import bigwig
import bbi

reload = False
16 17 18 19 20 21 22 23 24 25

app = Flask(__name__)
CORS(app)

@app.route('/', methods=['GET'])
def index():
    return "hi"

@app.route('/read-data', methods=['GET'])
def read_data():
Kruyff,D.L.W. (Dylan)'s avatar
Kruyff,D.L.W. (Dylan) committed
26 27 28 29
    size = bbi.chromsizes('test.bigWig')['chr1']
    bins = 100000
    data = bigwig.get('test.bigWig', 'chr1', 0, size, bins)
    print(data.shape)
30
    response = {
Kruyff,D.L.W. (Dylan)'s avatar
Kruyff,D.L.W. (Dylan) committed
31 32
        "index": list(range(0, size, int(size/(bins)))),
        "values": data.tolist()
33
    }
Kruyff,D.L.W. (Dylan)'s avatar
Kruyff,D.L.W. (Dylan) committed
34
    response = orjson.dumps(response)
35 36 37 38
    return response

@app.route('/create-windows', methods=['POST'])
def create_windows():
Kruyff,D.L.W. (Dylan)'s avatar
Kruyff,D.L.W. (Dylan) committed
39 40
    if reload:
        size = bbi.chromsizes('test.bigWig')['chr1']
41 42
        raw_data = request.json
        window_size = int(raw_data['parameters']["windowsize"])
Kruyff,D.L.W. (Dylan)'s avatar
Kruyff,D.L.W. (Dylan) committed
43 44 45 46 47 48 49 50 51
        data = bigwig.chunk(
            'test.bigWig',
            12000,
            12000 / window_size,
            12000 / 6,
            ['chr1'],
            verbose=True,
        )
        print(data.shape)
52 53
        np.save('processed-data', data)
    return '1'
54 55 56

@app.route('/create-tables', methods=['POST'])
def create_tables():
57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73
    t0 = time()
    print("loading")
    data = np.load('processed-data.npy')
    print(time()-t0)
    raw_data = orjson.loads(request.data)
    print(time()-t0)
    window_size = int(raw_data['parameters']["windowsize"])
    hash_size = int(raw_data['parameters']["hashsize"])
    table_size = int(raw_data['parameters']["tablesize"])
    data = np.array(data)
    print('Starting: ' + str(time()-t0))
    tables_hash_function = [np.random.uniform(-1, 1, size=(window_size, hash_size)) for _ in range(table_size)]
    print('Init time: ' + str(time() - t0))
    tables = []
    for index in range(table_size):
        t1 = time()
        table = defaultdict(list)
Kruyff,D.L.W. (Dylan)'s avatar
Kruyff,D.L.W. (Dylan) committed
74
        signatures_bool = np.dot(data, tables_hash_function[index]) > 0
75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91
        signatures = [''.join(['1' if x else '0' for x in lst]) for lst in signatures_bool]
        for i in range(len(signatures)):
            table[signatures[i]].append(i)
        print(time()-t1)
        tables.append(table)

    print('Creation time: ' + str(time() - t0))
    hash_functions = np.array(tables_hash_function).tolist()
    response = {}
    for table_index in range(table_size):
        response[str(table_index)] = {
            "hash": hash_functions[table_index],
            "entries": tables[table_index]
        }
    response = orjson.dumps(response)
    return response

92 93
@app.route('/query', methods=['POST'])
def query():
94
    t0 = time()
95
    raw_data = orjson.loads(request.data)
96
    window = raw_data['window']
Kruyff,D.L.W. (Dylan)'s avatar
Kruyff,D.L.W. (Dylan) committed
97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113
    if isinstance(window, int):
        output = np.load('processed-data.npy')[window]
        response = orjson.dumps(output.tolist())
        print("Query done: " + str(time() - t0))
        return response
    else :
        output = preprocessing.minmax_scale(window, (-1, 1))
        response = orjson.dumps(output.tolist())
        print("Query done: " + str(time()-t0))
        return response

@app.route('/window', methods=['POST'])
def window():
    t0 = time()
    raw_data = orjson.loads(request.data)
    indices = raw_data['indices']
    output = np.load('processed-data.npy')[indices]
114
    response = orjson.dumps(output.tolist())
Kruyff,D.L.W. (Dylan)'s avatar
Kruyff,D.L.W. (Dylan) committed
115 116
    print(output)
    print("Query done: " + str(time() - t0))
117 118 119 120 121 122 123
    return response

@app.route('/similarity', methods=['POST'])
def similarity():
    t0 = time()
    raw_data = orjson.loads(request.data)
    window = raw_data['query']
124 125 126
    tables = raw_data["tables"]
    neighbours = []

Kruyff,D.L.W. (Dylan)'s avatar
Kruyff,D.L.W. (Dylan) committed
127
    output = defaultdict(list)
128 129

    for t in tables.values():
Kruyff,D.L.W. (Dylan)'s avatar
Kruyff,D.L.W. (Dylan) committed
130
        signature_bool = np.dot(window, t["hash"]) > 0
131
        signature = ''.join(['1' if x else '0' for x in signature_bool])
132
        neighbours.extend(t["entries"][signature])
133 134
    neighbours_with_frequency = dict(Counter(neighbours))
    for index, frequency in neighbours_with_frequency.items():
135
        output[str(frequency)].append(index)
136
    response = orjson.dumps(output)
137 138 139 140 141 142 143 144 145 146 147
    print("Similarity done: " + str(time()-t0))
    return response

@app.route('/average-progress', methods=['POST'])
def average_progress():
    t0 = time()
    raw_data = orjson.loads(request.data)
    all_windows = raw_data['windows']
    data = np.load('processed-data.npy')
    output = []
    actual_windows = []
Kruyff,D.L.W. (Dylan)'s avatar
Kruyff,D.L.W. (Dylan) committed
148
    print("Starting average progress")
149 150 151
    print("Initialized: " + str(time() - t0))
    for windows in all_windows:
        t1 = time()
Kruyff,D.L.W. (Dylan)'s avatar
Kruyff,D.L.W. (Dylan) committed
152
        actual_windows.extend(data[windows])
153 154 155
        if len(actual_windows) == 0:
            output.append([])
            continue
156 157 158
        max_values = np.maximum.reduce(actual_windows).tolist()
        min_values = np.minimum.reduce(actual_windows).tolist()
        average_values = (np.sum(actual_windows, 0)/len(actual_windows)).tolist()
Kruyff,D.L.W. (Dylan)'s avatar
Kruyff,D.L.W. (Dylan) committed
159
        output = [({
160 161 162
            'average': average_values,
            'max': max_values,
            'min': min_values
Kruyff,D.L.W. (Dylan)'s avatar
Kruyff,D.L.W. (Dylan) committed
163
        })] + output
164 165 166
        print("Average calculated: " + str(time() - t1))
    response = orjson.dumps(output)
    print("Averages calculated: " + str(time() - t0))
167 168
    return response

169 170
@app.route('/average-table', methods=['POST'])
def average_table():
171 172 173 174 175
    t0 = time()
    raw_data = orjson.loads(request.data)
    all_windows = raw_data['windows']
    data = np.load('processed-data.npy')
    output = []
176
    print("Initialized: " + str(time() - t0))
177 178
    for windows in all_windows:
        t1 = time()
Kruyff,D.L.W. (Dylan)'s avatar
Kruyff,D.L.W. (Dylan) committed
179
        actual_windows = data[windows]
180
        print(len(actual_windows))
181 182 183 184 185 186 187
        average_values = np.average(actual_windows, 0)
        # average_values = (np.sum(actual_windows, 0) / len(actual_windows))
        std_values = np.std(actual_windows, 0)
        max_values = average_values + std_values
        min_values = average_values - std_values
        # max_values = np.maximum.reduce(actual_windows).tolist()
        # min_values = np.minimum.reduce(actual_windows).tolist()
188
        output.append({
189 190 191
            'average': average_values.tolist(),
            'max': max_values.tolist(),
            'min': min_values.tolist()
192
        })
193 194
        print("Average calculated: " + str(time() - t1))
    response = orjson.dumps(output)
195
    print("Averages calculated: " + str(time() - t0))
196 197 198 199 200
    return response

@app.route('/update', methods=['POST'])
def update():
    t0 = time()
201
    print("Start")
202
    raw_data = orjson.loads(request.data)
203 204
    print("Data loaded: " + str(time() - t0))
    data = np.load('processed-data.npy')
205 206
    label_data = raw_data["labelData"]
    tables = raw_data["tables"]
207
    window = raw_data["query"]
208 209 210 211 212 213

    window_size = int(raw_data['parameters']["windowsize"])
    hash_size = int(raw_data['parameters']["hashsize"])
    table_size = int(raw_data['parameters']["tablesize"])
    new_tables = []

214 215
    correct_indices = [int(index) for index, value in label_data.items() if value is True]
    incorrect_indices = [int(index) for index, value in label_data.items() if value is False]
216

217
    print("Initialized: " + str(time() - t0))
218 219
    for t in tables.values():
        valid = True
Kruyff,D.L.W. (Dylan)'s avatar
Kruyff,D.L.W. (Dylan) committed
220
        signature = ''.join((np.dot(window, t["hash"]) > 0).astype('int').astype('str'))
221
        neighbours = t["entries"][signature]
222 223 224 225 226 227 228 229 230 231
        for index in correct_indices:
            if index not in neighbours:
                valid = False
                break
        for index in incorrect_indices:
            if index in neighbours:
                valid = False
                break
        if valid:
            new_tables.append(t)
232 233 234 235 236 237
    print("Filtered good tables: " + str(time() - t0))
    for index in range(table_size - len(new_tables)):
        entries = defaultdict(list)
        t1 = time()
        while True:
            hash_function = np.random.randn(window_size, hash_size)
Kruyff,D.L.W. (Dylan)'s avatar
Kruyff,D.L.W. (Dylan) committed
238
            correct_signatures = [''.join((np.dot(data[i], hash_function) > 0).astype('int').astype('str')) for
239
                                  i in
240
                                  correct_indices]
241 242
            incorrect_signatures = [''.join((np.dot(data[i], hash_function) > 0).astype('int').astype('str')) for
                                    i
243 244 245 246 247 248 249
                                    in incorrect_indices]
            if correct_signatures.count(correct_signatures[0]) == len(
                    correct_signatures) and incorrect_signatures.count(
                    correct_signatures[0]) == 0:
                break
        print("first: " + str(time() - t1))
        t2 = time()
Kruyff,D.L.W. (Dylan)'s avatar
Kruyff,D.L.W. (Dylan) committed
250
        signatures_bool = np.dot(data, hash_function) > 0
251 252 253 254 255 256 257 258
        signatures = [''.join(['1' if x else '0' for x in lst]) for lst in signatures_bool]
        for i in range(len(signatures)):
            entries[signatures[i]].append(i)
        print("second: " + str(time() - t2))
        new_tables.append({
            "hash": hash_function.tolist(),
            "entries": entries
        })
259

260 261 262 263 264 265 266 267
    print('Update time: ' + str(time() - t0))
    response = {}
    for table_index in range(len(new_tables)):
        response[table_index] = {
            "hash": new_tables[table_index]["hash"],
            "entries": new_tables[table_index]["entries"]
        }
    response = jsonify(response)
268
    return response