├── .DS_Store ├── README.md ├── __pycache__ ├── data_fitter.cpython-310.pyc ├── data_handler.cpython-310.pyc ├── data_handler_base.cpython-310.pyc ├── data_handler_test.cpython-310-pytest-8.3.3.pyc ├── data_importer.cpython-310.pyc └── test_data_handler.cpython-310-pytest-8.3.3.pyc ├── data.db ├── data_fitter.py ├── data_handler.py ├── data_handler_base.py ├── dataset ├── ideal.csv ├── test.csv └── train.csv ├── main.py └── test_data_handler.py /.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Naruto-Leader/PyDataFilter/7b9b4e698b9d6afcca06eed1b48ef65cca0f5a5f/.DS_Store -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # DLMDSPWP01 - Programming with Python Assignment 2 | 3 | ## About The Project: 4 | 5 | The Python program uses training data to find four ideal functions. Then those four ideal functions are used to find the best fit for the test data. 6 | 7 | ## Built With: 8 | 9 | Pandas\ 10 | SQLAlchemy\ 11 | PyTest\ 12 | NumPy\ 13 | Matplotlib 14 | 15 | ## Contact: 16 | 17 | Matthew Presley - @mpresley - matthew.presley@iu-study.org\ 18 | Project Link: https://github.com/mpre5ley/PyDataFitter 19 | -------------------------------------------------------------------------------- /__pycache__/data_fitter.cpython-310.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Naruto-Leader/PyDataFilter/7b9b4e698b9d6afcca06eed1b48ef65cca0f5a5f/__pycache__/data_fitter.cpython-310.pyc -------------------------------------------------------------------------------- /__pycache__/data_handler.cpython-310.pyc: -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- /data_fitter.py: -------------------------------------------------------------------------------- 1 | from sqlalchemy.sql import text 2 | import pandas as pd 3 | import numpy as np 4 | 5 | class DataFitter: 6 | """ 7 | Class to fit data using the Sum of Squared Errors method 8 | 9 | Args: engine (Engine): An instance of the database engine object 10 | """ 11 | 12 | def __init__(self, engine): 13 | self.engine = engine 14 | self.best_fit_functions = [] 15 | 16 | def calculate_SSE(y_train, y_ideal): 17 | """ 18 | Calculate the sum of squared errors between the training data and ideal functions 19 | 20 | Args: 21 | y_train (float): y-coordinate from the training data 22 | y_ideal (float): y-coordinate from the ideal functions 23 | 24 | Returns: 25 | float: The sum of squared errors between the training data and ideal functions 26 | """ 27 | 28 | return np.sum((y_train - y_ideal)**2) 29 | 30 | def load_data(self, table_name): 31 | """ 32 | Load data from the database using engine object 33 | 34 | Args: 35 | table_name (str): The name of the table to load data from 36 | 37 | Returns: 38 | DataFrame: A Pandas DataFrame containing the data 39 | """ 40 | 41 | with self.engine.connect() as conn: 42 | result = conn.execute(text("SELECT * FROM " + table_name)) 43 | df = pd.DataFrame(result.fetchall(), columns=result.keys()) 44 | return df 45 | 46 | def fit_train_data(self, data_table, ideal_table): 47 | """ 48 | Finds ideal function that best fits the training function 49 | 50 | Args: 51 | data_table (str): The name of the table that holds the data 52 | ideal_table (str): The name of the table that holds the ideal functions 53 | 54 | Returns: 55 | None 56 | """ 57 | 58 | # Load data from database to Pandas DataFrame 59 | df_training = self.load_data(data_table) 60 | df_ideal = self.load_data(ideal_table) 61 | 62 | # Fit data to ideal functions 63 | for df_train_col in df_training.columns[1:]: 64 | best_fit_function = [] 65 | best_sse = float('inf') 66 | for df_ideal_col in df_ideal.columns[1:]: 67 | sse = np.sum((df_training[df_train_col] - df_ideal[df_ideal_col])**2) 68 | if sse < best_sse: 69 | best_sse = sse 70 | best_fit_function = df_ideal_col 71 | self.best_fit_functions.append(best_fit_function) 72 | 73 | 74 | def find_delta_y(self, best_fit_func, best_fit_df, test_data_df): 75 | """ 76 | Find deviation in Y coordinate between test data and best fit ideal functions 77 | 78 | Args: 79 | best_fit_func (list): A list of the best fit ideal functions 80 | best_fit_df (DataFrame): A Pandas DataFrame containing the best fit ideal functions x-y coordinates 81 | test_data_df (DataFrame): A Pandas DataFrame containing the test data x-y coordinates 82 | 83 | Returns: 84 | y_delta_num: A list containing the delta in Y coordinate between test data and best fit ideal functions 85 | y_delta_func: A list containing the name of the ideal function with the smallest y-coordinate deviation 86 | """ 87 | # Find deviation in Y coordinate between test data and best fit ideal functions, record the smallest deviation into lists 88 | y_delta_num = [] 89 | y_delta_func = [] 90 | for test_data_index in range(len(test_data_df)): 91 | for best_fit_col in best_fit_func[:-1]: 92 | for best_fit_df_index in range(len(best_fit_df)): 93 | if best_fit_df['x'][best_fit_df_index] == test_data_df['x'][test_data_index]: 94 | y_delta = abs(best_fit_df[best_fit_col][best_fit_df_index] - test_data_df['y'][test_data_index]) 95 | y_delta_col = best_fit_col 96 | break # When X coodinates match, break loop and compare y deviation 97 | # Compare the current deviation to the previous smallest deviation 98 | try: 99 | y_delta_small 100 | except NameError: 101 | y_delta_small = y_delta 102 | y_delta_col_small = y_delta_col 103 | else: 104 | if y_delta < y_delta_small: 105 | y_delta_small = y_delta 106 | y_delta_col_small = y_delta_col 107 | # Store y delta and ideal function name in lists for dataframe 108 | y_delta_num.append(y_delta_small) 109 | y_delta_func.append(y_delta_col_small) 110 | 111 | # Delete variables for next row of test data 112 | del y_delta_small 113 | del y_delta_col_small 114 | 115 | return y_delta_num, y_delta_func 116 | 117 | 118 | 119 | -------------------------------------------------------------------------------- /data_handler.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | from sqlalchemy.sql import text 3 | from data_handler_base import DataHandlerBase 4 | 5 | class DataHandler(DataHandlerBase): 6 | """ 7 | Class to import data into a database 8 | 9 | Args: session (Session): An instance of the database session object 10 | """ 11 | def __init__(self, session): 12 | super().__init__(session) 13 | 14 | def import_data(self, data, table_name): 15 | """ 16 | Imports data into a database table 17 | 18 | Args: 19 | data (DataFrame): The dataframe that holds the data to import 20 | table_name (str): The name of the table to create in the database 21 | 22 | Returns: 23 | None 24 | """ 25 | data.to_sql(table_name, self.session.bind, if_exists='replace', index=False) 26 | self.session.commit() 27 | 28 | def import_data_from_csv(self, file_path, table_name): 29 | """ 30 | Imports data from CSV into Pandas DataFrame 31 | 32 | Args: 33 | file_path (str): relative path of the CSV file 34 | table_name (str): The name of the table to create in the database 35 | 36 | Returns: 37 | None 38 | """ 39 | data = pd.read_csv(file_path) 40 | self.import_data(data, table_name) 41 | 42 | def load_list_to_df(self, db_engine, col_list): 43 | """ 44 | Imports list of columns from database table into Pandas DataFrame 45 | 46 | Args: 47 | db_engine (Engine): database engine object 48 | col_list (list): list of columns to import from database table 49 | 50 | Returns: 51 | DataFrame: A Pandas DataFrame containing the data 52 | """ 53 | df = pd.DataFrame(columns=col_list) 54 | with db_engine.connect() as conn: 55 | for col in col_list: 56 | col_list_result = conn.execute(text("SELECT " + col + " FROM ideal_function")) 57 | df[col] = col_list_result.fetchall() 58 | #convert tuples to floats 59 | for i in range(len(df[col])): 60 | df[col][i] = df[col][i][0] 61 | 62 | for col in col_list: 63 | df[col] = df[col].astype(float) 64 | return df 65 | 66 | def copy_table_to_df(self, db_engine, table_name): 67 | """ 68 | Load data from the database into a Pandas DataFrame 69 | 70 | Args: 71 | table_name (str): The name of the table to load data from 72 | 73 | Returns: 74 | DataFrame: A Pandas DataFrame containing the data 75 | """ 76 | with db_engine.connect() as conn: 77 | data_result = conn.execute(text("SELECT * FROM " + table_name)) 78 | 79 | df = pd.DataFrame(data_result.fetchall(), columns=data_result.keys()) 80 | return df 81 | -------------------------------------------------------------------------------- /data_handler_base.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | 3 | class DataHandlerBase: 4 | """ 5 | Base Class to handle data 6 | 7 | Args: session (Session): An instance of the database session object 8 | """ 9 | def __init__(self, session): 10 | self.session = session 11 | 12 | def import_data(self, data, table_name): 13 | pass 14 | 15 | def import_data_from_csv(self, file_path, table_name): 16 | pass 17 | 18 | def load_list_to_df(self, db_engine, col_list): 19 | pass 20 | 21 | def copy_table_to_df(self, db_engine, table_name): 22 | pass 23 | -------------------------------------------------------------------------------- /dataset/ideal.csv: -------------------------------------------------------------------------------- 1 | x,y1,y2,y3,y4,y5,y6,y7,y8,y9,y10,y11,y12,y13,y14,y15,y16,y17,y18,y19,y20,y21,y22,y23,y24,y25,y26,y27,y28,y29,y30,y31,y32,y33,y34,y35,y36,y37,y38,y39,y40,y41,y42,y43,y44,y45,y46,y47,y48,y49,y50 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2.3,-0.33190203,24.23826,11.946233,60.0893 226 | 2.4,0.09188771,27.971361,12.018673,58.252556 227 | 2.5,-0.16814248,31.696903,12.648813,57.43387 228 | 2.6,0.034154933,35.344273,13.430304,55.49318 229 | 2.7,0.11438007,39.603973,13.349541,53.799175 230 | 2.8,0.3421652,43.96778,13.952687,53.044525 231 | 2.9,0.5486801,48.621254,14.033558,51.5061 232 | 3.0,0.77308625,53.568745,14.958849,49.627136 233 | 3.1,1.5301938,59.76511,15.06092,48.97242 234 | 3.2,1.7722063,65.86679,15.695335,46.765446 235 | 3.3,1.5790944,71.71022,16.45906,45.470486 236 | 3.4,2.0347533,78.81827,16.519588,44.546036 237 | 3.5,2.1669905,85.403915,17.974937,43.00807 238 | 3.6,2.6904821,92.84224,17.502026,42.340942 239 | 3.7,2.3906581,100.86964,18.11371,40.2084 240 | 3.8,2.100779,109.36287,19.403967,39.209606 241 | 3.9,2.8729568,118.35969,19.94529,37.60588 242 | 4.0,3.418015,128.42453,19.730625,36.978916 243 | 4.1,2.8482835,138.12503,20.910408,35.82829 244 | 4.2,2.9537008,147.72116,21.238039,34.552235 245 | 4.3,3.6419778,159.08833,21.165085,33.220013 246 | 4.4,4.265741,169.94182,21.782143,31.725233 247 | 4.5,4.008899,182.58247,22.069466,30.304895 248 | 4.6,3.7222936,194.8732,22.68778,29.742687 249 | 4.7,4.274098,208.11002,23.267647,28.264437 250 | 4.8,5.0618944,221.6016,23.504873,27.154749 251 | 4.9,4.752725,235.05336,24.42293,25.773775 252 | 5.0,4.905665,250.3336,25.224699,24.794209 253 | 5.1,4.8713007,265.00754,25.655087,23.963673 254 | 5.2,5.7165895,281.01184,26.23071,22.072887 255 | 5.3,5.103345,297.45468,26.09131,21.961351 256 | 5.4,5.736775,315.0401,27.20083,21.001308 257 | 5.5,6.377877,333.00378,27.05729,19.52057 258 | 5.6,6.5307937,351.46548,27.833447,18.87061 259 | 5.7,6.0772567,370.1732,28.593563,17.724815 260 | 5.8,6.339171,389.7792,28.52428,16.787708 261 | 5.9,6.8272033,411.07748,29.403913,16.056564 262 | 6.0,6.673313,431.69946,30.380116,15.210613 263 | 6.1,6.755299,454.10114,30.600937,14.201845 264 | 6.2,7.423082,477.02863,31.454123,13.336014 265 | 6.3,8.067202,499.68115,31.2491,12.784313 266 | 6.4,7.80123,523.9182,31.750387,11.772338 267 | 6.5,7.658832,549.0036,32.676838,10.955556 268 | 6.6,8.568339,574.4985,32.51542,10.445071 269 | 6.7,8.771383,601.7586,33.401024,10.068517 270 | 6.8,8.470425,628.7006,34.35043,9.222325 271 | 6.9,8.527624,657.28845,34.932636,9.228201 272 | 7.0,9.044036,686.3899,34.754677,8.187652 273 | 7.1,8.830833,715.56,35.297585,7.8752565 274 | 7.2,9.431941,746.8428,36.422268,7.030091 275 | 7.3,9.448816,777.70197,36.671474,6.798119 276 | 7.4,9.402386,810.0036,36.6268,5.927882 277 | 7.5,9.856429,843.9888,37.264534,6.2560086 278 | 7.6,10.406261,878.0819,37.943096,5.7789245 279 | 7.7,10.483953,912.82623,38.012424,4.819468 280 | 7.8,10.903358,949.1458,38.60413,4.7285256 281 | 7.9,10.323647,986.30237,39.930634,4.20937 282 | 8.0,11.236141,1024.2325,40.358112,4.119519 283 | 8.1,10.920326,1062.6277,40.66814,3.3694506 284 | 8.2,11.796623,1103.0417,40.860924,3.7972562 285 | 8.3,11.598334,1143.8048,41.220318,3.4973335 286 | 8.4,12.0387945,1185.5217,41.951843,2.5839827 287 | 8.5,11.842022,1228.3228,42.152855,2.390747 288 | 8.6,12.522043,1272.0402,42.544807,2.2551057 289 | 8.7,12.855869,1316.7098,43.987335,2.6373868 290 | 8.8,13.009679,1363.3068,43.661743,1.8718067 291 | 8.9,13.061979,1410.1509,44.658703,1.610579 292 | 9.0,12.668264,1457.5944,44.908535,1.749655 293 | 9.1,13.317889,1506.796,45.001057,2.166051 294 | 9.2,13.345668,1557.746,45.805813,1.6625733 295 | 9.3,13.506583,1608.8472,46.671005,1.8769232 296 | 9.4,13.535542,1660.9835,47.366234,1.0995753 297 | 9.5,13.862246,1714.5338,47.56391,0.8855721 298 | 9.6,13.964414,1769.4869,48.30456,0.71895754 299 | 9.7,14.237361,1825.1898,48.365276,0.80129576 300 | 9.8,14.368036,1882.883,49.27811,0.6776254 301 | 9.9,15.271646,1940.1332,49.246155,0.6593184 302 | 10.0,15.245541,1999.9031,50.235046,0.9532806 303 | 10.1,15.193747,2060.8538,50.790752,1.2285326 304 | 10.2,15.326085,2122.732,50.98844,0.2837537 305 | 10.3,15.681278,2185.603,51.413197,0.6681263 306 | 10.4,16.055222,2249.835,51.77788,1.2150499 307 | 10.5,15.516224,2315.5847,52.517487,0.60221493 308 | 10.6,15.9585705,2382.3892,53.25238,0.6214623 309 | 10.7,16.856203,2450.374,53.41953,0.72492594 310 | 10.8,16.630577,2519.2944,54.252342,0.60344225 311 | 10.9,16.631283,2589.6921,54.276386,0.4716834 312 | 11.0,16.940863,2662.1104,54.68714,0.5516239 313 | 11.1,17.357515,2735.2817,55.57471,1.535253 314 | 11.2,17.113403,2809.6313,56.443607,1.7269373 315 | 11.3,17.639929,2885.613,56.123325,1.27288 316 | 11.4,18.28821,2962.6758,56.594975,1.4601041 317 | 11.5,17.838253,3041.5444,57.94431,2.1505957 318 | 11.6,17.915905,3121.807,57.864426,2.4530191 319 | 11.7,18.884508,3203.6877,58.487362,2.4473364 320 | 11.8,18.396336,3285.5837,59.009537,2.8112354 321 | 11.9,18.624561,3369.9924,59.503086,2.4392986 322 | 12.0,19.027494,3456.2346,60.412907,3.431336 323 | 12.1,19.39343,3543.3584,60.566902,3.5065122 324 | 12.2,19.568026,3631.3481,61.05232,3.6626382 325 | 12.3,19.230003,3721.4985,61.940945,4.7884665 326 | 12.4,20.15367,3813.3696,62.485855,4.9654484 327 | 12.5,20.25169,3905.9768,62.97045,5.1394124 328 | 12.6,19.717548,4000.7922,62.5022,5.4382243 329 | 12.7,20.673906,4096.5547,63.87355,5.9373064 330 | 12.8,21.09865,4194.766,63.993107,6.806631 331 | 12.9,20.5579,4292.91,64.42855,7.667781 332 | 13.0,20.598501,4393.5957,65.3833,8.324691 333 | 13.1,20.712814,4496.047,65.00611,8.68141 334 | 13.2,21.850136,4599.9927,65.80682,9.059501 335 | 13.3,21.546925,4705.719,66.07462,10.4398365 336 | 13.4,22.004425,4811.896,67.03218,11.291765 337 | 13.5,22.498127,4921.089,67.28333,11.440074 338 | 13.6,22.567713,5031.2188,68.048485,12.193721 339 | 13.7,21.934498,5143.2036,68.0316,12.987708 340 | 13.8,22.283297,5256.625,69.2234,14.4890375 341 | 13.9,22.754929,5371.4136,69.368256,14.515507 342 | 14.0,22.586683,5487.677,70.12506,16.345198 343 | 14.1,23.44767,5606.473,70.82975,16.979795 344 | 14.2,23.670996,5726.212,70.88342,17.738539 345 | 14.3,23.925362,5848.667,71.3628,18.209484 346 | 14.4,23.807688,5971.5186,72.07946,19.49972 347 | 14.5,23.52467,6097.648,72.73531,20.328037 348 | 14.6,24.591185,6224.29,73.0586,21.331665 349 | 14.7,24.896029,6353.248,73.81486,23.094545 350 | 14.8,24.577204,6483.6387,74.22524,23.546997 351 | 14.9,24.722559,6615.523,74.009895,24.920847 352 | 15.0,24.871653,6749.6826,75.430214,25.807438 353 | 15.1,24.905693,6886.1353,75.22872,26.659164 354 | 15.2,25.614841,7023.5864,76.1867,27.913784 355 | 15.3,25.335499,7163.1313,76.49628,28.791384 356 | 15.4,26.281654,7304.923,76.74465,29.937378 357 | 15.5,26.333672,7447.8027,77.82526,30.751514 358 | 15.6,26.046892,7592.612,78.04847,32.130257 359 | 15.7,26.455336,7740.23,78.02457,33.21023 360 | 15.8,26.368628,7888.967,79.47276,34.59991 361 | 15.9,27.15283,8039.2373,79.86095,35.866 362 | 16.0,27.129084,8191.616,79.8729,37.21782 363 | 16.1,26.751871,8346.723,80.76296,37.681404 364 | 16.2,27.36485,8502.828,80.804825,38.905994 365 | 16.3,27.477352,8661.496,81.41493,40.210957 366 | 16.4,28.295033,8821.42,82.44391,41.60834 367 | 16.5,28.441545,8984.257,82.51089,43.015625 368 | 16.6,27.706556,9148.443,83.18286,43.751213 369 | 16.7,28.750156,9314.479,83.30909,45.461464 370 | 16.8,28.70776,9483.183,84.30296,46.493694 371 | 16.9,28.504154,9653.318,84.44001,47.531292 372 | 17.0,28.578192,9826.228,84.63577,49.401424 373 | 17.1,29.446218,10000.62,85.30306,50.120823 374 | 17.2,29.518734,10176.584,86.214966,51.566555 375 | 17.3,29.337793,10355.69,86.75623,53.56638 376 | 17.4,29.559355,10536.49,87.33917,54.964657 377 | 17.5,30.382164,10718.677,87.65341,56.241364 378 | 17.6,29.750975,10904.044,87.617615,57.525143 379 | 17.7,30.73722,11090.118,88.87676,59.094006 380 | 17.8,30.661835,11279.324,89.19227,60.50199 381 | 17.9,30.847145,11470.894,89.2391,61.595287 382 | 18.0,31.090006,11663.734,90.0789,63.641296 383 | 18.1,31.40743,11859.015,90.0714,64.614426 384 | 18.2,31.137447,12056.959,90.97422,66.67435 385 | 18.3,31.376091,12256.522,91.5396,68.44134 386 | 18.4,32.177235,12458.709,92.14657,69.571884 387 | 18.5,32.397163,12663.301,92.87676,71.29373 388 | 18.6,32.125023,12869.295,92.57208,72.54649 389 | 18.7,32.265812,13078.676,93.251274,74.93135 390 | 18.8,32.883892,13289.322,93.718636,75.997505 391 | 18.9,33.100853,13502.5,94.034256,78.684395 392 | 19.0,32.779934,13718.116,94.960846,79.53923 393 | 19.1,33.43027,13935.828,95.57979,81.42586 394 | 19.2,32.966526,14155.423,95.83025,83.5419 395 | 19.3,33.485565,14377.99,96.24772,85.2605 396 | 19.4,34.072723,14602.365,97.15841,87.965195 397 | 19.5,34.15051,14829.701,97.719,89.044914 398 | 19.6,33.875698,15058.578,98.09285,91.52288 399 | 19.7,33.952816,15291.142,98.50072,93.63963 400 | 19.8,34.51727,15524.978,99.343155,95.835785 401 | 19.9,34.63903,15761.66,99.192604,97.69748 402 | -------------------------------------------------------------------------------- /main.py: -------------------------------------------------------------------------------- 1 | # code can be found at https://github.com/mpre5ley/PyDataFitter 2 | 3 | from sqlalchemy import create_engine 4 | from sqlalchemy.orm import declarative_base, sessionmaker 5 | from data_handler import DataHandler 6 | from data_fitter import DataFitter 7 | import matplotlib.pyplot as plt 8 | 9 | def create_session(db_engine): 10 | """ 11 | Returns an instance of the database session configuration 12 | 13 | Args: 14 | db_engine (Engine): An instance of the database engine object 15 | 16 | Returns: 17 | session_instance (Session): An instance of the database session 18 | configuration 19 | """ 20 | base = declarative_base() 21 | base.metadata.create_all(db_engine) 22 | session = sessionmaker(bind=db_engine) 23 | session_instance = session() 24 | return session_instance 25 | 26 | def main(): 27 | """ Main function to run the program """ 28 | 29 | # Create a SQLite database engine 30 | db_engine = create_engine('sqlite:///data.db') 31 | # Create a session 32 | session_instance = create_session(db_engine) 33 | # Create model to move data to database 34 | data_import = DataHandler(session_instance) 35 | # Import data from CSVs to database 36 | data_import.import_data_from_csv('./dataset/train.csv', 'training_data') 37 | data_import.import_data_from_csv('./dataset/ideal.csv', 'ideal_function') 38 | data_import.import_data_from_csv('./dataset/test.csv', 'test_data') 39 | 40 | # Fit training data to find 4 ideal functions 41 | data_fit = DataFitter(db_engine) 42 | data_fit.fit_train_data('training_data', 'ideal_function') 43 | best_fit_func = data_fit.best_fit_functions 44 | # Add x column for ideal function x coordinate 45 | best_fit_func.append('x') 46 | 47 | # Load best fit ideal functions from database into dataframe 48 | best_fit_df = data_import.load_list_to_df(db_engine, best_fit_func) 49 | 50 | # Load test data from database into dataframe 51 | test_data_df = data_import.copy_table_to_df(db_engine, 'test_data') 52 | 53 | # Load training data from database into dataframe for visualization 54 | train_data_df = data_import.copy_table_to_df(db_engine, 'training_data') 55 | 56 | # Find deviation in Y coordinate between test data and best fit ideal functions 57 | y_delta_num, y_delta_func = data_fit.find_delta_y(best_fit_func, best_fit_df, test_data_df) 58 | test_data_df['Delta Y (test func)'] = y_delta_num 59 | test_data_df['No. of ideal func'] = y_delta_func 60 | 61 | # Import test data with deviation in Y coordinate and ideal function number into database 62 | data_import.import_data(test_data_df, 'test_data') 63 | 64 | # Visualize training data, best fit ideal functions, and test data 65 | test_plot = test_data_df.plot(x='x', y='y', kind='scatter', rot=45, title='Test Data and Best Fit Ideal Function') 66 | best_fit_df.plot(x='x', y='y40', kind='line', rot=45, ax=test_plot) 67 | best_fit_df.plot(x='x', y=['y13', 'y24', 'y36', 'y40'], kind='line', rot=45, subplots=True, title = 'Best Fit Ideal Functions') 68 | train_data_df.plot(x='x', y=['y1', 'y2', 'y3', 'y4'], kind='line', rot=45, title='Training Data', subplots=True) 69 | plt.show() 70 | 71 | # Close the session and dispose the engine 72 | session_instance.close() 73 | db_engine.dispose() 74 | 75 | if __name__ == "__main__": 76 | main() -------------------------------------------------------------------------------- /test_data_handler.py: -------------------------------------------------------------------------------- 1 | from data_handler import DataHandler 2 | from sqlalchemy import create_engine 3 | from sqlalchemy.orm import declarative_base, sessionmaker 4 | 5 | def create_session(db_engine): 6 | """ 7 | Returns an instance of the database session configuration 8 | 9 | Args: 10 | db_engine (Engine): An instance of the database engine object 11 | 12 | Returns: 13 | session_instance (Session): An instance of the database session 14 | configuration 15 | """ 16 | base = declarative_base() 17 | base.metadata.create_all(db_engine) 18 | session = sessionmaker(bind=db_engine) 19 | session_instance = session() 20 | return session_instance 21 | 22 | def test_can_load_list_to_df(): 23 | """ 24 | Tests that the function to load a list of columns from 25 | the database to a dataframe works 26 | 27 | Args: 28 | None 29 | 30 | Returns: 31 | None 32 | """ 33 | # Create a SQLite database engine 34 | db_engine = create_engine('sqlite:///data.db') 35 | # Create a session 36 | session_instance = create_session(db_engine) 37 | # Create model to move data to database 38 | data = DataHandler(session_instance) 39 | # create random list of columns from ideal function table 40 | col_list = ['y1', 'y2', 'y3'] 41 | test_df = data.load_list_to_df(db_engine, col_list) 42 | for col in col_list: 43 | assert col in test_df.columns 44 | 45 | 46 | --------------------------------------------------------------------------------