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How to use pretty_param method in pandera

Best Python code snippet using pandera_python

functions.py

Source:functions.py

...169            largest=True,170            short=True,171        )172    )173def pretty_param(param, value=None):174    if isinstance(param, str):175        return unit_of.get(param.split(".")[0], "{}").format(value)176    elif isinstance(param, dict):177        return [178            f"{param_}: {pretty_param(param_, value_)}"179            for param_, value_ in param.items()180        ]181    else:182        print(f"{NAME}.pretty_param({param.__class__.__name__}), class not found.")183def pretty_shape(shape):184    """describe shape as a string.185    Args:186        shape (List[int]): shape.187    Returns:188        str: shape as string.189    """190    return "x".join([str(value) for value in list(shape)])191def pretty_shape_of_matrix(matrix):192    """describe size of matrix as a string.193    Args:194        matrix (Any): matrix.195    Returns:196        str: size of matrix....

elasticsearch.py

Source:elasticsearch.py

...19                                    not create new cluster!")20@require_basic_auth21class ElasticsearchClusterInitHandler(ElasticSearchBaseHandler):22    def post(self):23        param = self.pretty_param()24        cluster_name = param['clusterName']25        self.check_cluster(cluster_name)26        self.elastic_op.init_cluster(param)27        self.finish({"message": "creating cluster successful!"})28@require_basic_auth29class ElasticsearchNodeInitHandler(ElasticSearchBaseHandler):30    def post(self):31        param = self.pretty_param()32        self.elastic_op.init_node(param)33        self.finish({"message": "creating cluster successful!"})34@require_basic_auth35class ElasticsearchNodeSyncHandler(ElasticSearchBaseHandler):36    def post(self):37        param = self.pretty_param()38        zk_op = self.get_zkoper()39        if zk_op.cluster_exists(param['clusterName']):40            self.elastic_op.sync_node(param['clusterName'])41        self.finish({"message": "sync cluster info successful!"})42@require_basic_auth43class ElasticsearchConfigHandler(ElasticSearchBaseHandler):44    """45    function: start node46    url example: curl --user root:root -d "" "http://localhost:9999/elasticsearch/config"47    """48    def post(self):49        param = self.pretty_param()50        es_heap_size = int(param.get('es_heap_size', ES_HEAP_SIZE))51        if es_heap_size < ES_HEAP_SIZE:52            self.set_status(500)53            self.finish({"message": "para not valid!"})54            return55        self.elastic_op.config()56        self.elastic_op.sys_config(57            es_heap_size='%dg' % (es_heap_size / ES_HEAP_SIZE))58        self.finish({"message": "config cluster successful!"})59@require_basic_auth60class Elasticsearch_Start_Handler(ElasticSearchBaseHandler):61    def post(self):62        """63        function: start node...

visualizations.py

Source:visualizations.py

1from typing import List, Tuple2import matplotlib.pyplot as plt3import numpy as np4import pandas as pd5import seaborn as sns6def plot_metric_score_variation(7    data: pd.DataFrame, param: str, colors: list,8    xytext_locs: List[Tuple[int, int]], scale_y_axis: bool = True9) -> None:10    pretty_param = param.split('__')[1]11    titles = [12        'F-Score', 'G-Mean', 'Precision',13        'Recall', 'ROC AUC', 'Specificity']14    # get list of metrics to plot15    metrics_to_plot = [16        col.replace('mean_test_', '')17        for col in data.columns18        if col.startswith('mean_test_')]19    # plot metric score variation in relation with a param change by experiment20    fig = plt.figure(figsize=[10, 13])21    plt.suptitle(f'VariaciÃ³n de "{pretty_param}"', fontsize=14)22    plot_params = {'data': data, 'x': param}23    for index, metric in enumerate(metrics_to_plot):24        test_metric = f'mean_test_{metric}'25        train_metric = f'mean_train_{metric}'26        plt.subplot(3, 2, index+1)27        sns.lineplot(28            **plot_params,29            y=test_metric,30            label=f'test',31            color=colors[0])32        ax = sns.lineplot(33            **plot_params,34            y=train_metric,35            label=f'train',36            color=colors[1])37        set_lineplot_annotation(ax, colors, xytext_locs)38        plt.legend().remove()39        plt.title(titles[index], fontsize=14)40        plt.xlabel(pretty_param)41        plt.ylabel('puntuaciÃ³n')42        plt.ylim([0, 1]) if scale_y_axis else None43    handles, labels = ax.get_legend_handles_labels()44    fig.legend(  # title='Legenda'45        handles, labels, loc='upper center',46        bbox_to_anchor=(0.5, 0.965),47        ncol=2, fancybox=True, shadow=False,48        facecolor='white', edgecolor='grey')49    plt.tight_layout()50def set_lineplot_annotation(ax, colors: list, xytext_locs: List[Tuple[int, int]] = None) -> None:51    tex_locs = ['top', 'bottom']52    xytext_locs = xytext_locs or [(0, 0), (0, 0)]53    annotate_params = {54        'xytext': (0, 0),55        'textcoords': "offset points",56        'ha': 'center',57        'weight': 'bold',58        'bbox': {59            'boxstyle': 'round,pad=0.3',60            'fc': 'white',61            'alpha': 0.5}}62    for i, line in enumerate(ax.lines):63        annotate_params.update(64            {'xytext': xytext_locs[i], 'va': tex_locs[i], 'color': colors[i]})65        y_max = np.max(line.get_ydata())66        max_index = np.where(line.get_ydata() == y_max)[0][0]67        x_max = line.get_xdata()[max_index]68        ax.annotate(69            '{:.2f}%'.format(y_max*100),70            (x_max, y_max),71            **annotate_params)72        y_min = np.min(line.get_ydata())73        min_index = np.where(line.get_ydata() == y_min)[0][0]74        x_min = line.get_xdata()[min_index]75        ax.annotate(76            '{:.2f}%'.format(y_min*100),77            (x_min, y_min),...

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