-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathrequests.py
More file actions
189 lines (124 loc) · 5.83 KB
/
Copy pathrequests.py
File metadata and controls
189 lines (124 loc) · 5.83 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
import numpy as np
import pandas as pd
from flask import request
from pandas.core.dtypes.common import is_string_dtype
from sklearn import linear_model
from MathScience import Statistics, Tables
from Utils import Helpers
def upload_report():
file = request.files['file']
try:
file_name = Helpers.save_file(file)
except (Exception, ) as e:
return Helpers.generate_error_response(str(e))
return Helpers.generate_successful_response(href=file_name)
def get_table():
file_name = request.args['file']
table_type = request.args['type']
try:
data = Helpers.convert_dataframe_to_dict({
'source': Tables.get_source_table,
'normalized': Tables.get_normalized_table,
'statistic': Tables.get_statistic_table,
'chi_square': Tables.get_chi_square_table,
'correlation': Tables.get_correlation_table,
'student': Tables.get_student_table,
'student_partial': Tables.get_partial_student_table,
'partial_correlation': Tables.get_partial_correlation_table
}[table_type](file_name))
return Helpers.generate_successful_response(data=data)
except (Exception, ) as e:
return Helpers.generate_error_response(str(e))
def get_intervals():
file_name = request.args['file']
try:
data = Helpers.get_charts_data(file_name)
return Helpers.generate_successful_response(data=data)
except (Exception, ) as e:
return Helpers.generate_error_response(str(e))
def get_linear_regression_coefficients_matrix():
file_name = request.args['file']
y_column = request.args['y']
try:
linear_regression_coefficients = Tables.get_linear_regression_coefficients(file_name, y_column)
response_data_frame = Helpers.convert_dataframe_to_dict(linear_regression_coefficients)
return Helpers.generate_successful_response(data=response_data_frame)
except (Exception, ) as e:
return Helpers.generate_error_response(str(e))
def get_linear_regression_coefficients():
file_name = request.args['file']
y = request.args['y']
data_frame = Tables.get_normalized_table(file_name)
incorrect_columns = list()
for column in data_frame.columns:
if is_string_dtype(data_frame[column]):
incorrect_columns.append(column)
data_frame = data_frame.drop(labels=incorrect_columns, axis=1)
y_data_frame = data_frame[y]
x_data_frame = data_frame.drop(labels=y, axis=1)
regression = linear_model.LinearRegression()
regression.fit(x_data_frame, y_data_frame)
return Helpers.generate_successful_response(data={
'coef': [regression.intercept_, *regression.coef_.tolist()],
'intercept': regression.intercept_.tolist()})
def get_regression_student_coefficients_matrix():
file_name = request.args['file']
y = request.args['y']
file_name = request.args['file']
y = request.args['y']
data_frame = Tables.get_normalized_table(file_name)
data_frame = Helpers.remove_string_columns(data_frame)
y_data_frame = data_frame[y]
x_data_frame = data_frame.drop(labels=y, axis=1)
regression = linear_model.LinearRegression()
regression.fit(x_data_frame, y_data_frame)
result_dict = {
'Параметр': [f'b{i + 1}' for i in range(len(regression.coef_))],
't': []
}
columns = x_data_frame.columns
try:
linear_regression_coefficients = regression.coef_
for i in range(len(columns)):
result_dict['t'].append(linear_regression_coefficients[i] / Statistics.average_sampling_error(x_data_frame[columns[i]]))
linear_regression_student_data_frame = pd.DataFrame(result_dict)
response_dict = Helpers.convert_dataframe_to_dict(linear_regression_student_data_frame)
return Helpers.generate_successful_response(data=response_dict)
except (Exception,) as e:
return Helpers.generate_error_response(str(e))
# Not work
def get_regression_fault():
file_name = request.args['file']
y = request.args['y']
data_frame = Tables.get_normalized_table(file_name)
incorrect_columns = list()
for column in data_frame.columns:
if is_string_dtype(data_frame[column]):
incorrect_columns.append(column)
data_frame = data_frame.drop(labels=incorrect_columns, axis=1)
y_data_frame = data_frame[y]
x_data_frame = data_frame.drop(labels=y, axis=1)
regression = linear_model.LinearRegression()
regression.fit(x_data_frame, y_data_frame)
result_dict = {'Исходное значение': [],
'Полученное значение': [],
'Погрешность': []}
x_data_frame = x_data_frame
y_data_frame = y_data_frame.values.tolist()
for i, column in enumerate(x_data_frame.columns):
source_value = y_data_frame[i]
result_dict['Исходное значение'].append(source_value)
predicted_value = regression.predict(x_data_frame[column])
result_dict['Полученное значение'].append(predicted_value)
result_dict['Погрешность'].append(abs(source_value - predicted_value))
response_data_frame = pd.DataFrame(result_dict)
return Helpers.generate_successful_response(data=Helpers.convert_dataframe_to_dict(response_data_frame))
def get_multiple_correlation_coefficients():
file_name = request.args['file']
response = Helpers.convert_dataframe_to_dict(Tables.get_multiple_correlation_coefficients_table(file_name))
return Helpers.generate_successful_response(data=response)
def get_phisher_regression_coefficients():
file_name = request.args['file']
y = request.args['y']
response = Helpers.convert_dataframe_to_dict(Tables.get_phisher_correlation_coefficients_table(file_name, y))
return Helpers.generate_successful_response(data=response)