└── PyCaret Classification Crash Course.ipynb /PyCaret Classification Crash Course.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# 1. Install and Import Dependencies" 8 | ] 9 | }, 10 | { 11 | "cell_type": "code", 12 | "execution_count": null, 13 | "metadata": {}, 14 | "outputs": [], 15 | "source": [ 16 | "!pip install pycaret pandas shap" 17 | ] 18 | }, 19 | { 20 | "cell_type": "code", 21 | "execution_count": 2, 22 | "metadata": {}, 23 | "outputs": [], 24 | "source": [ 25 | "import pandas as pd\n", 26 | "from pycaret.classification import *" 27 | ] 28 | }, 29 | { 30 | "cell_type": "markdown", 31 | "metadata": {}, 32 | "source": [ 33 | "# 2. Load Data" 34 | ] 35 | }, 36 | { 37 | "cell_type": "code", 38 | "execution_count": 3, 39 | "metadata": {}, 40 | "outputs": [], 41 | "source": [ 42 | "df = pd.read_csv('heart.csv')" 43 | ] 44 | }, 45 | { 46 | "cell_type": "code", 47 | "execution_count": 4, 48 | "metadata": {}, 49 | "outputs": [ 50 | { 51 | "data": { 52 | "text/html": [ 53 | "
\n", 54 | "\n", 67 | "\n", 68 | " \n", 69 | " \n", 70 | " \n", 71 | " \n", 72 | " \n", 73 | " \n", 74 | " \n", 75 | " \n", 76 | " \n", 77 | " \n", 78 | " \n", 79 | " \n", 80 | " \n", 81 | " \n", 82 | " \n", 83 | " \n", 84 | " \n", 85 | " \n", 86 | " \n", 87 | " \n", 88 | " \n", 89 | " \n", 90 | " \n", 91 | " \n", 92 | " \n", 93 | " \n", 94 | " \n", 95 | " \n", 96 | " \n", 97 | " \n", 98 | " \n", 99 | " \n", 100 | " \n", 101 | " \n", 102 | " \n", 103 | " \n", 104 | " \n", 105 | " \n", 106 | " \n", 107 | " \n", 108 | " \n", 109 | " \n", 110 | " \n", 111 | " \n", 112 | " \n", 113 | " \n", 114 | " \n", 115 | " \n", 116 | " \n", 117 | " \n", 118 | " \n", 119 | " \n", 120 | " \n", 121 | " \n", 122 | " \n", 123 | " \n", 124 | " \n", 125 | " \n", 126 | " \n", 127 | " \n", 128 | " \n", 129 | " \n", 130 | " \n", 131 | " \n", 132 | " \n", 133 | " \n", 134 | " \n", 135 | " \n", 136 | " \n", 137 | " \n", 138 | " \n", 139 | " \n", 140 | " \n", 141 | " \n", 142 | " \n", 143 | " \n", 144 | " \n", 145 | " \n", 146 | " \n", 147 | " \n", 148 | " \n", 149 | " \n", 150 | " \n", 151 | " \n", 152 | " \n", 153 | " \n", 154 | " \n", 155 | " \n", 156 | " \n", 157 | " \n", 158 | " \n", 159 | " \n", 160 | " \n", 161 | " \n", 162 | " \n", 163 | " \n", 164 | " \n", 165 | " \n", 166 | " \n", 167 | " \n", 168 | " \n", 169 | " \n", 170 | " \n", 171 | " \n", 172 | " \n", 173 | " \n", 174 | "
agesexcptrestbpscholfbsrestecgthalachexangoldpeakslopecathaltarget
063131452331015002.30011
137121302500118703.50021
241011302040017201.42021
356111202360117800.82021
457001203540116310.62021
\n", 175 | "
" 176 | ], 177 | "text/plain": [ 178 | " age sex cp trestbps chol fbs restecg thalach exang oldpeak slope \\\n", 179 | "0 63 1 3 145 233 1 0 150 0 2.3 0 \n", 180 | "1 37 1 2 130 250 0 1 187 0 3.5 0 \n", 181 | "2 41 0 1 130 204 0 0 172 0 1.4 2 \n", 182 | "3 56 1 1 120 236 0 1 178 0 0.8 2 \n", 183 | "4 57 0 0 120 354 0 1 163 1 0.6 2 \n", 184 | "\n", 185 | " ca thal target \n", 186 | "0 0 1 1 \n", 187 | "1 0 2 1 \n", 188 | "2 0 2 1 \n", 189 | "3 0 2 1 \n", 190 | "4 0 2 1 " 191 | ] 192 | }, 193 | "execution_count": 4, 194 | "metadata": {}, 195 | "output_type": "execute_result" 196 | } 197 | ], 198 | "source": [ 199 | "df.head()" 200 | ] 201 | }, 202 | { 203 | "cell_type": "code", 204 | "execution_count": 5, 205 | "metadata": { 206 | "collapsed": true 207 | }, 208 | "outputs": [ 209 | { 210 | "data": { 211 | "text/plain": [ 212 | "age int64\n", 213 | "sex int64\n", 214 | "cp int64\n", 215 | "trestbps int64\n", 216 | "chol int64\n", 217 | "fbs int64\n", 218 | "restecg int64\n", 219 | "thalach int64\n", 220 | "exang int64\n", 221 | "oldpeak float64\n", 222 | "slope int64\n", 223 | "ca int64\n", 224 | "thal int64\n", 225 | "target int64\n", 226 | "dtype: object" 227 | ] 228 | }, 229 | "execution_count": 5, 230 | "metadata": {}, 231 | "output_type": "execute_result" 232 | } 233 | ], 234 | "source": [ 235 | "df.dtypes" 236 | ] 237 | }, 238 | { 239 | "cell_type": "markdown", 240 | "metadata": {}, 241 | "source": [ 242 | "# 3. Train and Evaluate Model" 243 | ] 244 | }, 245 | { 246 | "cell_type": "code", 247 | "execution_count": 6, 248 | "metadata": {}, 249 | "outputs": [], 250 | "source": [ 251 | "cat_features = ['sex', 'cp', 'fbs', 'restecg', 'exang', 'thal']" 252 | ] 253 | }, 254 | { 255 | "cell_type": "code", 256 | "execution_count": 8, 257 | "metadata": { 258 | "collapsed": true 259 | }, 260 | "outputs": [ 261 | { 262 | "data": { 263 | "text/html": [ 264 | "\n", 266 | " \n", 267 | " \n", 268 | " \n", 269 | " \n", 270 | " \n", 271 | " \n", 272 | " \n", 273 | " \n", 274 | " \n", 275 | " \n", 276 | " \n", 277 | " \n", 278 | " \n", 279 | " \n", 280 | " \n", 281 | " \n", 282 | " \n", 283 | " \n", 284 | " \n", 285 | " \n", 286 | " \n", 287 | " \n", 288 | " \n", 289 | " \n", 290 | " \n", 291 | " \n", 292 | " \n", 293 | " \n", 294 | " \n", 295 | " \n", 296 | " \n", 297 | " \n", 298 | " \n", 299 | " \n", 300 | " \n", 301 | " \n", 302 | " \n", 303 | " \n", 304 | " \n", 305 | " \n", 306 | " \n", 307 | " \n", 308 | " \n", 309 | " \n", 310 | " \n", 311 | " \n", 312 | " \n", 313 | " \n", 314 | " \n", 315 | " \n", 316 | " \n", 317 | " \n", 318 | " \n", 319 | " \n", 320 | " \n", 321 | " \n", 322 | " \n", 323 | " \n", 324 | " \n", 325 | " \n", 326 | " \n", 327 | " \n", 328 | " \n", 329 | " \n", 330 | " \n", 331 | " \n", 332 | " \n", 333 | " \n", 334 | " \n", 335 | " \n", 336 | " \n", 337 | " \n", 338 | " \n", 339 | " \n", 340 | " \n", 341 | " \n", 342 | " \n", 343 | " \n", 344 | " \n", 345 | " \n", 346 | " \n", 347 | " \n", 348 | " \n", 349 | " \n", 350 | " \n", 351 | " \n", 352 | " \n", 353 | " \n", 354 | " \n", 355 | " \n", 356 | " \n", 357 | " \n", 358 | " \n", 359 | " \n", 360 | " \n", 361 | " \n", 362 | " \n", 363 | " \n", 364 | " \n", 365 | " \n", 366 | " \n", 367 | " \n", 368 | " \n", 369 | " \n", 370 | " \n", 371 | " \n", 372 | " \n", 373 | " \n", 374 | " \n", 375 | " \n", 376 | " \n", 377 | " \n", 378 | " \n", 379 | " \n", 380 | " \n", 381 | " \n", 382 | " \n", 383 | " \n", 384 | " \n", 385 | " \n", 386 | " \n", 387 | " \n", 388 | " \n", 389 | " \n", 390 | " \n", 391 | " \n", 392 | " \n", 393 | " \n", 394 | " \n", 395 | " \n", 396 | " \n", 397 | " \n", 398 | " \n", 399 | " \n", 400 | " \n", 401 | " \n", 402 | " \n", 403 | " \n", 404 | " \n", 405 | " \n", 406 | " \n", 407 | " \n", 408 | " \n", 409 | " \n", 410 | " \n", 411 | " \n", 412 | " \n", 413 | " \n", 414 | " \n", 415 | " \n", 416 | " \n", 417 | " \n", 418 | " \n", 419 | " \n", 420 | " \n", 421 | " \n", 422 | " \n", 423 | " \n", 424 | " \n", 425 | " \n", 426 | " \n", 427 | " \n", 428 | " \n", 429 | " \n", 430 | " \n", 431 | " \n", 432 | " \n", 433 | " \n", 434 | " \n", 435 | " \n", 436 | " \n", 437 | " \n", 438 | " \n", 439 | " \n", 440 | " \n", 441 | " \n", 442 | " \n", 443 | " \n", 444 | " \n", 445 | " \n", 446 | " \n", 447 | " \n", 448 | " \n", 449 | " \n", 450 | " \n", 451 | " \n", 452 | " \n", 453 | " \n", 454 | " \n", 455 | " \n", 456 | " \n", 457 | " \n", 458 | " \n", 459 | " \n", 460 | " \n", 461 | " \n", 462 | " \n", 463 | " \n", 464 | " \n", 465 | " \n", 466 | " \n", 467 | " \n", 468 | " \n", 469 | " \n", 470 | " \n", 471 | " \n", 472 | " \n", 473 | " \n", 474 | " \n", 475 | " \n", 476 | " \n", 477 | " \n", 478 | " \n", 479 | " \n", 480 | " \n", 481 | " \n", 482 | " \n", 483 | " \n", 484 | " \n", 485 | " \n", 486 | " \n", 487 | " \n", 488 | " \n", 489 | " \n", 490 | " \n", 491 | " \n", 492 | " \n", 493 | " \n", 494 | " \n", 495 | " \n", 496 | " \n", 497 | " \n", 498 | " \n", 499 | " \n", 500 | " \n", 501 | " \n", 502 | " \n", 503 | " \n", 504 | " \n", 505 | " \n", 506 | " \n", 507 | " \n", 508 | " \n", 509 | " \n", 510 | " \n", 511 | " \n", 512 | " \n", 513 | " \n", 514 | " \n", 515 | " \n", 516 | " \n", 517 | " \n", 518 | " \n", 519 | " \n", 520 | " \n", 521 | " \n", 522 | " \n", 523 | " \n", 524 | " \n", 525 | " \n", 526 | " \n", 527 | " \n", 528 | " \n", 529 | " \n", 530 | " \n", 531 | " \n", 532 | " \n", 533 | " \n", 534 | " \n", 535 | " \n", 536 | " \n", 537 | " \n", 538 | " \n", 539 | " \n", 540 | " \n", 541 | " \n", 542 | " \n", 543 | " \n", 544 | " \n", 545 | " \n", 546 | " \n", 547 | " \n", 548 | " \n", 549 | " \n", 550 | " \n", 551 | " \n", 552 | " \n", 553 | " \n", 554 | " \n", 555 | " \n", 556 | " \n", 557 | " \n", 558 | " \n", 559 | " \n", 560 | " \n", 561 | "
Description Value
0session_id8760
1Targettarget
2Target TypeBinary
3Label Encoded0: 0, 1: 1
4Original Data(303, 14)
5Missing ValuesFalse
6Numeric Features5
7Categorical Features8
8Ordinal FeaturesFalse
9High Cardinality FeaturesFalse
10High Cardinality MethodNone
11Transformed Train Set(212, 27)
12Transformed Test Set(91, 27)
13Shuffle Train-TestTrue
14Stratify Train-TestFalse
15Fold GeneratorStratifiedKFold
16Fold Number10
17CPU Jobs-1
18Use GPUFalse
19Log ExperimentFalse
20Experiment Nameclf-default-name
21USI8cc5
22Imputation Typesimple
23Iterative Imputation IterationNone
24Numeric Imputermean
25Iterative Imputation Numeric ModelNone
26Categorical Imputerconstant
27Iterative Imputation Categorical ModelNone
28Unknown Categoricals Handlingleast_frequent
29NormalizeFalse
30Normalize MethodNone
31TransformationFalse
32Transformation MethodNone
33PCAFalse
34PCA MethodNone
35PCA ComponentsNone
36Ignore Low VarianceFalse
37Combine Rare LevelsFalse
38Rare Level ThresholdNone
39Numeric BinningFalse
40Remove OutliersFalse
41Outliers ThresholdNone
42Remove MulticollinearityFalse
43Multicollinearity ThresholdNone
44ClusteringFalse
45Clustering IterationNone
46Polynomial FeaturesFalse
47Polynomial DegreeNone
48Trignometry FeaturesFalse
49Polynomial ThresholdNone
50Group FeaturesFalse
51Feature SelectionFalse
52Feature Selection Methodclassic
53Features Selection ThresholdNone
54Feature InteractionFalse
55Feature RatioFalse
56Interaction ThresholdNone
57Fix ImbalanceFalse
58Fix Imbalance MethodSMOTE
" 562 | ], 563 | "text/plain": [ 564 | "" 565 | ] 566 | }, 567 | "metadata": {}, 568 | "output_type": "display_data" 569 | } 570 | ], 571 | "source": [ 572 | "experiment = setup(df, target='target', categorical_features=cat_features)" 573 | ] 574 | }, 575 | { 576 | "cell_type": "code", 577 | "execution_count": 9, 578 | "metadata": {}, 579 | "outputs": [ 580 | { 581 | "data": { 582 | "text/html": [ 583 | "\n", 603 | " \n", 604 | " \n", 605 | " \n", 606 | " \n", 607 | " \n", 608 | " \n", 609 | " \n", 610 | " \n", 611 | " \n", 612 | " \n", 613 | " \n", 614 | " \n", 615 | " \n", 616 | " \n", 617 | " \n", 618 | " \n", 619 | " \n", 620 | " \n", 621 | " \n", 622 | " \n", 623 | " \n", 624 | " \n", 625 | " \n", 626 | " \n", 627 | " \n", 628 | " \n", 629 | " \n", 630 | " \n", 631 | " \n", 632 | " \n", 633 | " \n", 634 | " \n", 635 | " \n", 636 | " \n", 637 | " \n", 638 | " \n", 639 | " \n", 640 | " \n", 641 | " \n", 642 | " \n", 643 | " \n", 644 | " \n", 645 | " \n", 646 | " \n", 647 | " \n", 648 | " \n", 649 | " \n", 650 | " \n", 651 | " \n", 652 | " \n", 653 | " \n", 654 | " \n", 655 | " \n", 656 | " \n", 657 | " \n", 658 | " \n", 659 | " \n", 660 | " \n", 661 | " \n", 662 | " \n", 663 | " \n", 664 | " \n", 665 | " \n", 666 | " \n", 667 | " \n", 668 | " \n", 669 | " \n", 670 | " \n", 671 | " \n", 672 | " \n", 673 | " \n", 674 | " \n", 675 | " \n", 676 | " \n", 677 | " \n", 678 | " \n", 679 | " \n", 680 | " \n", 681 | " \n", 682 | " \n", 683 | " \n", 684 | " \n", 685 | " \n", 686 | " \n", 687 | " \n", 688 | " \n", 689 | " \n", 690 | " \n", 691 | " \n", 692 | " \n", 693 | " \n", 694 | " \n", 695 | " \n", 696 | " \n", 697 | " \n", 698 | " \n", 699 | " \n", 700 | " \n", 701 | " \n", 702 | " \n", 703 | " \n", 704 | " \n", 705 | " \n", 706 | " \n", 707 | " \n", 708 | " \n", 709 | " \n", 710 | " \n", 711 | " \n", 712 | " \n", 713 | " \n", 714 | " \n", 715 | " \n", 716 | " \n", 717 | " \n", 718 | " \n", 719 | " \n", 720 | " \n", 721 | " \n", 722 | " \n", 723 | " \n", 724 | " \n", 725 | " \n", 726 | " \n", 727 | " \n", 728 | " \n", 729 | " \n", 730 | " \n", 731 | " \n", 732 | " \n", 733 | " \n", 734 | " \n", 735 | " \n", 736 | " \n", 737 | " \n", 738 | " \n", 739 | " \n", 740 | " \n", 741 | " \n", 742 | " \n", 743 | " \n", 744 | " \n", 745 | " \n", 746 | " \n", 747 | " \n", 748 | " \n", 749 | " \n", 750 | " \n", 751 | " \n", 752 | " \n", 753 | " \n", 754 | " \n", 755 | " \n", 756 | " \n", 757 | " \n", 758 | " \n", 759 | "
Model Accuracy AUC Recall Prec. F1 Kappa MCC TT (Sec)
ridgeRidge Classifier0.82990.00000.87880.83380.84880.65180.66720.0030
ldaLinear Discriminant Analysis0.82510.90580.86970.83100.84280.64260.65810.0030
lrLogistic Regression0.82060.90150.86060.82480.83840.63340.64400.3060
nbNaive Bayes0.82030.88050.87880.81310.84240.63300.64230.0030
etExtra Trees Classifier0.81080.88450.83710.83330.82690.61690.63270.0850
rfRandom Forest Classifier0.80650.88180.84550.81150.82290.60650.61650.0950
adaAda Boost Classifier0.77790.80870.82650.78830.80270.54530.55470.0130
lightgbmLight Gradient Boosting Machine0.77290.86550.81820.78790.79660.53370.54430.0080
gbcGradient Boosting Classifier0.74960.84260.79320.76400.77260.48840.49680.0100
dtDecision Tree Classifier0.67400.66810.71520.70210.70320.33680.34150.0030
knnK Neighbors Classifier0.66540.66760.69770.69830.69560.32330.32630.2130
qdaQuadratic Discriminant Analysis0.65650.65110.66210.70630.66360.30350.32840.0040
svmSVM - Linear Kernel0.62270.00000.81890.64470.69190.21510.23020.0060
" 760 | ], 761 | "text/plain": [ 762 | "" 763 | ] 764 | }, 765 | "metadata": {}, 766 | "output_type": "display_data" 767 | } 768 | ], 769 | "source": [ 770 | "best_model = compare_models()" 771 | ] 772 | }, 773 | { 774 | "cell_type": "markdown", 775 | "metadata": {}, 776 | "source": [ 777 | "# 4. Test Model" 778 | ] 779 | }, 780 | { 781 | "cell_type": "code", 782 | "execution_count": 13, 783 | "metadata": {}, 784 | "outputs": [ 785 | { 786 | "data": { 787 | "text/html": [ 788 | "
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agesexcptrestbpscholfbsrestecgthalachexangoldpeakslopecathaltargetLabel
29857001402410112310.210300
29945131102640113201.210301
30068101441931114103.412300
30157101301310111511.211300
30257011302360017400.011201
\n", 916 | "
" 917 | ], 918 | "text/plain": [ 919 | " age sex cp trestbps chol fbs restecg thalach exang oldpeak \\\n", 920 | "298 57 0 0 140 241 0 1 123 1 0.2 \n", 921 | "299 45 1 3 110 264 0 1 132 0 1.2 \n", 922 | "300 68 1 0 144 193 1 1 141 0 3.4 \n", 923 | "301 57 1 0 130 131 0 1 115 1 1.2 \n", 924 | "302 57 0 1 130 236 0 0 174 0 0.0 \n", 925 | "\n", 926 | " slope ca thal target Label \n", 927 | "298 1 0 3 0 0 \n", 928 | "299 1 0 3 0 1 \n", 929 | "300 1 2 3 0 0 \n", 930 | "301 1 1 3 0 0 \n", 931 | "302 1 1 2 0 1 " 932 | ] 933 | }, 934 | "execution_count": 13, 935 | "metadata": {}, 936 | "output_type": "execute_result" 937 | } 938 | ], 939 | "source": [ 940 | "predict_model(best_model, df.tail())" 941 | ] 942 | }, 943 | { 944 | "cell_type": "markdown", 945 | "metadata": {}, 946 | "source": [ 947 | "# 5. Save Model" 948 | ] 949 | }, 950 | { 951 | "cell_type": "code", 952 | "execution_count": 16, 953 | "metadata": { 954 | "collapsed": true 955 | }, 956 | "outputs": [ 957 | { 958 | "name": "stdout", 959 | "output_type": "stream", 960 | "text": [ 961 | "Transformation Pipeline and Model Succesfully Saved\n" 962 | ] 963 | }, 964 | { 965 | "data": { 966 | "text/plain": [ 967 | "(Pipeline(memory=None,\n", 968 | " steps=[('dtypes',\n", 969 | " DataTypes_Auto_infer(categorical_features=['sex', 'cp', 'fbs',\n", 970 | " 'restecg', 'exang',\n", 971 | " 'thal'],\n", 972 | " display_types=True, features_todrop=[],\n", 973 | " id_columns=[],\n", 974 | " ml_usecase='classification',\n", 975 | " numerical_features=[], target='target',\n", 976 | " time_features=[])),\n", 977 | " ('imputer',\n", 978 | " Simple_Imputer(categorical_strategy='not_available',\n", 979 | " fill_value_categorical=Non...\n", 980 | " ('fix_perfect', Remove_100(target='target')),\n", 981 | " ('clean_names', Clean_Colum_Names()),\n", 982 | " ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),\n", 983 | " ('dfs', 'passthrough'), ('pca', 'passthrough'),\n", 984 | " ['trained_model',\n", 985 | " RidgeClassifier(alpha=1.0, class_weight=None, copy_X=True,\n", 986 | " fit_intercept=True, max_iter=None,\n", 987 | " normalize=False, random_state=8760,\n", 988 | " solver='auto', tol=0.001)]],\n", 989 | " verbose=False),\n", 990 | " 'ridge-model.pkl')" 991 | ] 992 | }, 993 | "execution_count": 16, 994 | "metadata": {}, 995 | "output_type": "execute_result" 996 | } 997 | ], 998 | "source": [ 999 | "save_model(best_model, model_name='ridge-model')" 1000 | ] 1001 | }, 1002 | { 1003 | "cell_type": "code", 1004 | "execution_count": 18, 1005 | "metadata": {}, 1006 | "outputs": [ 1007 | { 1008 | "name": "stdout", 1009 | "output_type": "stream", 1010 | "text": [ 1011 | "Transformation Pipeline and Model Successfully Loaded\n" 1012 | ] 1013 | } 1014 | ], 1015 | "source": [ 1016 | "model = load_model('ridge-model')" 1017 | ] 1018 | }, 1019 | { 1020 | "cell_type": "code", 1021 | "execution_count": 19, 1022 | "metadata": {}, 1023 | "outputs": [ 1024 | { 1025 | "data": { 1026 | "text/plain": [ 1027 | "array([0, 1, 0, 0, 1])" 1028 | ] 1029 | }, 1030 | "execution_count": 19, 1031 | "metadata": {}, 1032 | "output_type": "execute_result" 1033 | } 1034 | ], 1035 | "source": [ 1036 | "model.predict(df.tail())" 1037 | ] 1038 | } 1039 | ], 1040 | "metadata": { 1041 | "kernelspec": { 1042 | "display_name": "pycaret", 1043 | "language": "python", 1044 | "name": "pycaret" 1045 | }, 1046 | "language_info": { 1047 | "codemirror_mode": { 1048 | "name": "ipython", 1049 | "version": 3 1050 | }, 1051 | "file_extension": ".py", 1052 | "mimetype": "text/x-python", 1053 | "name": "python", 1054 | "nbconvert_exporter": "python", 1055 | "pygments_lexer": "ipython3", 1056 | "version": "3.7.3" 1057 | } 1058 | }, 1059 | "nbformat": 4, 1060 | "nbformat_minor": 2 1061 | } 1062 | --------------------------------------------------------------------------------