main.py 6.81 KB
Newer Older
1
2
3
4
5
from flask import Flask, jsonify, request
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from flask_cors import CORS
6
from collections import defaultdict, Counter
7
from time import time
8
9
10
11
12
13
14
import dask.dataframe as dd
import os.path
import json
from sklearn import preprocessing
from functools import partial
from itertools import groupby
from multiprocessing import Pool
15
16
17
18
19
20
21
22
23
24

app = Flask(__name__)
CORS(app)

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

@app.route('/read-data', methods=['GET'])
def read_data():
25
26
27
28
29
30
31
32
33
34
35
36
    filename = 'processed-data.pkl'
    if (not os.path.isfile(filename)):
        print("start")
        df = dd.read_csv("NW_Ground_Stations_2016.csv", usecols=['number_sta', 'date', 't'])
        print("read file")
        df = df.loc[df['number_sta'] == 14066001]
        print("split rows")
        df = df.compute()
        df.to_pickle(filename)
        print("to_pandas")
    df = pd.read_pickle(filename)
    df.dropna(subset=['t'], inplace=True)
37
    response = {
38
39
        "index": json.dumps(df.loc[:, 'date'].values.astype(str).tolist()),
        "values": json.dumps(df.loc[:, 't'].values.astype(str).tolist())
40
    }
41
    print("response ready")
42
43
44
    response = jsonify(response)
    return response

45
46
47
48
49
50
51
52
53
54
55
56
# @app.route('/read-data', methods=['GET'])
# def read_data():
#     df = pd.read_csv("1.csv", index_col=3)
#     df.index = pd.to_datetime(df.index)
#     df.sort_index(inplace=True)
#     meantemp = df.loc[:, 7].copy()
#     response = {
#         "index": meantemp.index.values.astype(str).tolist(),
#         "values": meantemp.values.tolist()
#     }
#     response = jsonify(response)
#     return response
57
58
59
60
61

@app.route('/create-windows', methods=['POST'])
def create_windows():
    raw_data = request.json
    values = raw_data["values"]
62
    window_size = int(raw_data['parameters']["windowsize"])
63
64
65
    data = [values[i:i+window_size] for i in range(len(values) - window_size)]
    data = preprocessing.minmax_scale(data, (-1, 1), axis=1)
    response = jsonify(data.tolist())
66
67
    return response

68
69
70
71
72
73
74
75
76
def fill_table(data, hash_functions, index):
    table = defaultdict(list)
    signatures = [''.join((np.dot(data[window_index], hash_functions[index]) > 0).astype('int').astype('str')) for window_index in
                  range(data.shape[0])]
    counted_sig = enumerate(signatures)
    for i, x in counted_sig:
        table[x].append(i)
    return table

77
78
79
@app.route('/create-tables', methods=['POST'])
def create_tables():
    t0 = time()
80
81
    raw_data = request.json
    data = raw_data["windows"]
82
83
84
    window_size = int(raw_data['parameters']["windowsize"])
    hash_size = int(raw_data['parameters']["hashsize"])
    table_size = int(raw_data['parameters']["tablesize"])
85
    data = np.array(data)
86
87
88
89
90
91
92
93
94
95
96
    tables_hash_function = [np.random.uniform(-1, 1, size=(window_size, hash_size)) for _ in range(table_size)]
    print('Init time: ' + str(time() - t0))

    try:
        pool = Pool()
        func = partial(fill_table, data, tables_hash_function)
        print('Starting pool: ' + str(time() - t0))
        tables = pool.map(func, range(table_size))
    finally:
        pool.close()
        pool.join()
97

98
99
100
101
102
103
104
105
    print('Creation time: ' + str(time() - t0))
    hash_functions = np.array(tables_hash_function).tolist()
    response = {}
    for table_index in range(table_size):
        response[table_index] = {
            "hash": hash_functions[table_index],
            "entries": tables[table_index]
        }
106
107
108
109
110
111
112
113
114
115
    response = jsonify(response)
    return response

@app.route('/query', methods=['POST'])
def query():
    raw_data = request.json
    window = raw_data["window"]
    tables = raw_data["tables"]
    neighbours = []

116
117
118
    output = {}

    for t in tables.values():
119
120
        signature = ''.join((np.dot(window, t["hash"]) > 0).astype('int').astype('str'))
        neighbours.extend(t["entries"][signature])
121
122
123
124
125
126
127
128
    neighbours_with_frequency = dict(Counter(neighbours))
    for index, frequency in neighbours_with_frequency.items():
        if not frequency in output:
            output[frequency] = []
        output[frequency].append(index)
    response = jsonify(output)
    return response

129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
def create_valid_table(data, window_size, hash_size, correct_indices, incorrect_indices, index):
    entries = defaultdict(list)
    while True:
        hash_function = np.random.randn(window_size, hash_size)
        correct_signatures = [''.join((np.dot(data[index], hash_function) > 0).astype('int').astype('str')) for index in
                              correct_indices]
        incorrect_signatures = [''.join((np.dot(data[index], hash_function) > 0).astype('int').astype('str')) for index
                                in incorrect_indices]
        if correct_signatures.count(correct_signatures[0]) == len(correct_signatures) and incorrect_signatures.count(
                correct_signatures[0]) == 0:
            break
    for window_index in range(data.shape[0]):
        signature = ''.join((np.dot(data[window_index], hash_function) > 0).astype('int').astype('str'))
        entries[signature].append(window_index)
    return {
        "hash": hash_function.tolist(),
        "entries": entries
    }

148
149
150
151
@app.route('/update', methods=['POST'])
def update():
    t0 = time()
    raw_data = request.json
152

153
154
155
156
157
158
159
160
161
162
    data = raw_data["windows"]
    data = np.array(data)
    label_data = raw_data["labelData"]
    tables = raw_data["tables"]

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

163
164
    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]
165
166
167
168
169

    window = data[correct_indices[0]]

    for t in tables.values():
        valid = True
170
171
        signature = ''.join((np.dot(window, t["hash"]) > 0).astype('int').astype('str'))
        neighbours = t["entries"][signature]
172
173
174
175
176
177
178
179
180
181
182
        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)

183
184
185
186
187
188
189
190
191
    try:
        pool = Pool()
        func = partial(create_valid_table, data, window_size, hash_size, correct_indices, incorrect_indices)
        print('Starting pool: ' + str(time() - t0))
        new_tables.extend(pool.map(func, range(table_size - len(new_tables))))
    finally:
        pool.close()
        pool.join()

192
193
194
195
196
197
198
199
    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)
200
    return response