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How to use results_dir method in avocado

Best Python code snippet using avocado_python

machine_learning.smk

Source:machine_learning.smk

1rule train_test_split:2    input:3        config["dataset_dir"] + "dataset/full_classification.npy"4    output:5        config["dataset_dir"] + "dataset/train.h5py"6    threads:7        18    message:9        "Splitting training data into test and train dataset"10    log:11        config["results_dir"] + "logs/train_test_split/train_test_split.log"12    benchmark:13        config["results_dir"] + "benchmarks/train_test_split/train_test_split.benchmark.txt"14    script:15        "../scripts/machine_learning/train_test_split.py"16rule sgd_classifier:17    input:18        config["dataset_dir"] + "dataset/train.h5py"19    output:20        model=config["results_dir"] + "models/SGD.sav",21        metrics=config["results_dir"] + "model_metrics/SGD.sav",22        report=config["results_dir"] + "model_metrics/SGD_report.sav"23    threads:24        125    message:26        "Training SGD classifier"27    log:28        config["results_dir"] + "logs/models/sgd_classifier.log"29    benchmark:30        config["results_dir"] + "benchmarks/models/sgd_classifier.benchmark.txt"31    script:32        "../scripts/machine_learning/sgd_classifier.py"33rule sgd_classifier_manual:34    input:35        config["dataset_dir"] + "dataset/train.h5py"36    output:37        model=config["results_dir"] + "models/SGDmanual.sav",38        metrics=config["results_dir"] + "model_metrics/SGDmanual.sav",39        report=config["results_dir"] + "model_metrics/SGDmanual_report.sav"40    threads:41        142    message:43        "Training SGD classifier"44    log:45        config["results_dir"] + "logs/models/sgd_manual_classifier.log"46    benchmark:47        config["results_dir"] + "benchmarks/models/sgd_manual_classifier.benchmark.txt"48    script:49        "../scripts/machine_learning/sgd_classifier_manual.py"50rule gaussian_nb:51    input:52        config["dataset_dir"] + "dataset/train.h5py"53    output:54        model=config["results_dir"] + "models/GaussianNB.sav",55        metrics=config["results_dir"] + "model_metrics/GaussianNB.sav",56        report=config["results_dir"] + "model_metrics/GaussianNB_report.sav"57    threads:58        159    message:60        "Training Gaussian Naive Bayes classifier"61    log:62        config["results_dir"] + "logs/models/gaussian_nb.log"63    benchmark:64        config["results_dir"] + "benchmarks/models/gaussian_nb.benchmark.txt"65    script:66        "../scripts/machine_learning/gaussiannb.py"67rule zero_r:68    input:69        config["dataset_dir"] + "dataset/train.h5py"70    output:71        model=config["results_dir"] + "models/ZeroR.sav",72        metrics=config["results_dir"] + "model_metrics/ZeroR.sav",73        report=config["results_dir"] + "model_metrics/ZeroR_report.sav"74    threads:75        176    message:77        "Training ZeroR classifier"78    log:79        config["results_dir"] + "logs/models/zero_r.log"80    benchmark:81        config["results_dir"] + "benchmarks/models/zero_r.benchmark.txt"82    script:83        "../scripts/machine_learning/zero_r.py"84rule multinomial_nb:85    input:86        config["dataset_dir"] + "dataset/train.h5py"87    output:88        model=config["results_dir"] + "models/MultinomialNB.sav",89        metrics=config["results_dir"] + "model_metrics/MultinomialNB.sav",90        report=config["results_dir"] + "model_metrics/MultinomialNB_report.sav"91    threads:92        193    message:94        "Training Multinomial Naive Bayes classifier"95    log:96        config["results_dir"] + "logs/models/multinomial_nb.log"97    benchmark:98        config["results_dir"] + "benchmarks/models/multinomial_nb.benchmark.txt"99    script:100        "../scripts/machine_learning/multinomialnb.py"101rule decision_tree:102    input:103        config["dataset_dir"] + "dataset/train.h5py"104    output:105        model=config["results_dir"] + "models/DecisionTree.sav",106        metrics=config["results_dir"] + "model_metrics/DecisionTree.sav",107        report=config["results_dir"] + "model_metrics/DecisionTree_report.sav"108    threads:109        1110    message:111        "Training Decision Tree classifier"112    log:113        config["results_dir"] + "logs/models/DecisionTree.log"114    benchmark:115        config["results_dir"] + "benchmarks/models/DecisionTree.benchmark.txt"116    script:117        "../scripts/machine_learning/decisiontree.py"118rule SVM:119    input:120        config["dataset_dir"] + "dataset/train.h5py"121    output:122        model=config["results_dir"] + "models/SVM.sav",123        metrics=config["results_dir"] + "model_metrics/SVM.sav",124        report=config["results_dir"] + "model_metrics/SVM_report.sav"125    threads:126        1127    message:128        "Training SVM classifier"129    log:130        config["results_dir"] + "logs/models/SVM.log"131    benchmark:132        config["results_dir"] + "benchmarks/models/SVM.benchmark.txt"133    script:134        "../scripts/machine_learning/SVM.py"135rule SVM_sampling:136    input:137        config["dataset_dir"] + "dataset/full_classification.npy"138    output:139        model=config["results_dir"] + "models/SVMsampling.sav",140        metrics=config["results_dir"] + "model_metrics/SVMsampling.sav",141        report=config["results_dir"] + "model_metrics/SVMsampling_report.sav"142    threads:143        1144    message:145        "Training SVM_sampling classifier"146    log:147        config["results_dir"] + "logs/models/SVMsampling.log"148    benchmark:149        config["results_dir"] + "benchmarks/models/SVMsampling.benchmark.txt"150    script:151        "../scripts/machine_learning/SVM_sampling.py"152# rule Kernel:153#     input:154#         config["dataset_dir"] + "dataset/train.h5py"155#     output:156#         model=config["results_dir"] + "models/Kernel.sav",157#         metrics=config["results_dir"] + "model_metrics/Kernel.sav"158#     threads:159#         1160#     message:161#         "Training SVM classifier"162#     log:163#         config["results_dir"] + "logs/models/Kernel.log"164#     benchmark:165#         config["results_dir"] + "benchmarks/models/Kernel.benchmark.txt"166#     script:...

main.py

Source:main.py

1import os2import shutil3from pathlib import Path4from plotting import plot_counts_by_question_type, plot_count_over_time, plot_counts_by_team_and_member5from utils import get_chat_data, get_talk_data, calculate_participation_score6def handle_chats(filename, results_dir, rounds, round_names, category_names):7    teams = get_chat_data(filename)8    plot_counts_by_question_type(teams, "Chat Count by question type and category", rounds, category_names, round_names,9                                 os.path.join(results_dir,10                                              "chats_vs_category_qtype.png") if results_dir is not None else None)11    plot_count_over_time(teams, "Chat Count vs time for each team",12                         os.path.join(results_dir, "chats_count_vs_time.png") if results_dir is not None else None)13    plot_counts_by_team_and_member(teams, 'Chat count by team member',14                                   os.path.join(results_dir,15                                                "chats_vs_member_teams.png") if results_dir is not None else None)16    calculate_participation_score(teams, os.path.join(results_dir,17                                                "chat_participation_scores.txt") if results_dir is not None else None)18def handle_talks(filename, results_dir, rounds, round_names, category_names):19    teams = get_talk_data(filename)20    plot_counts_by_question_type(teams, "Talk Activity by question type and category", rounds, category_names,21                                 round_names, os.path.join(results_dir,22                                                           "talks_vs_category_qtype.png") if results_dir is not None else None)23    plot_count_over_time(teams, "Talk Activity vs time for each team",24                         os.path.join(results_dir, "talks_count_vs_time.png") if results_dir is not None else None)25    plot_counts_by_team_and_member(teams, 'Talk Activity by team member',26                                   os.path.join(results_dir,27                                                "talks_vs_member_teams.png") if results_dir is not None else None)28    calculate_participation_score(teams, os.path.join(results_dir,29                                                "talk_participation_scores.txt") if results_dir is not None else None)30if __name__ == "__main__":31    rounds = [32        [[1664431635289, 1664431799384], [1664431799384, 1664431963479], [1664431963479, 1664432127574],33         [1664432127574, 1664432291670]],34        [[1664432678581, 1664432806428], [1664432806428, 1664432934275], [1664432934275, 1664433062122],35         [1664433062122, 1664433189971]]36    ]37    round_names = ["picture", "category", "trivia", "who am i"]38    category_names = ["geography", "science"]39    results_dir = "results"40    if results_dir is not None:41        if os.path.exists(results_dir):  shutil.rmtree(results_dir)42        Path(results_dir).mkdir(exist_ok=True)43    handle_chats("chat_data_2.json", results_dir, rounds, round_names, category_names)44    handle_talks("talk_data_2.json", results_dir, rounds, round_names, category_names)45    # geography = 1664431635289 -> 166443229167046    # picture = 1664431635289 -> 166443179938447    # category = 1664431799384 -> 166443196347948    # trivia = 1664431963479 -> 166443212757449    # whoami = 1664432127574 -> 166443229167050    # science = 1664432678581 -> 166443318997151    # picture = 1664432678581 -> 166443280642852    # category = 1664432806428 -> 166443293427553    # trivia = 1664432934275 -> 1664433062122...

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