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How to use create_metrics_plot method in autotest

Best Python code snippet using autotest_python

create_graphs.py

Source:create_graphs.py

...5from matplotlib import pyplot as plt6current_folder = '/home/2014-0353_generaleye/Huy/YOLOv3/Main/Visualizations/'7input_file1 = '/home/2014-0353_generaleye/Huy/YOLOv3/Main/scores_all.xlsx'8input_file2 = 'PR_all.xlsx'9def create_metrics_plot(df_plot):10    with sns.plotting_context('paper', font_scale=1.3):11        g = sns.FacetGrid(df_plot, col='Metrics', hue='Model', col_wrap=2, palette='tab10', height=5, aspect=1.5, despine=False)12        g = g.map(sns.lineplot, 'Threshold', 'Score')13        g.set_titles(row_template = '{row_name}', col_template = '{col_name}')14        g.add_legend(frameon=True)15        16    return g17    18def create_PR_curve(df_plot, df_F1_model):19    with sns.plotting_context('paper', font_scale=1.3):20        g = sns.FacetGrid(df_plot, col='Threshold', hue='Model', col_wrap=2, palette='tab10', height=5, aspect=1, despine=False)    21        for i in range(len(list_f1)):22            g = g.map(sns.lineplot, 'Recall', df_plot.columns[4+i], color='grey', alpha=0.1)23            txt_loc = (recall_cutoff-0.01, df_plot.loc[df_plot['Recall'] > recall_cutoff-0.01, df_plot.columns[4+i]].unique()+0.02)24            for ax in g.axes.ravel():25                ax.text(*txt_loc, df_plot.columns[4+i], horizontalalignment='right', size='small', color='grey', alpha=0.7)26        g = g.map(sns.lineplot, 'Recall', 'Precision')27        df_AP = df_plot.groupby(['Model', 'Threshold']).agg({'Precision': lambda x: np.nansum(x[0:101:10])/11}).reset_index()28        df_AP = df_AP.merge(df_F1_model, on=['Model', 'Threshold'])29        df_AP['Precision'] = df_AP['Precision'].round(2)30        df_AP['Model'] = pd.Categorical(df_AP['Model'], [x.lstrip('Model_') for x in list_models2])31        df_AP = df_AP.sort_values('Model')32        df_AP['Model'] = df_AP['Model'].replace({'COCO_Lab': 'COCO', 'Synth_Lab': 'Synth', 'Synth_DA_Lab': 'Synth_DA'})33        for ax, threshold in zip(g.axes.ravel(), df_plot['Threshold'].unique()):34            df_temp = df_AP.loc[df_AP['Threshold'] == threshold, ['Model', 'Precision', 'F1']]35            loc_legend = 'upper right' if threshold>=0.9 else 'lower left'36            ax.legend(loc=loc_legend,37                      handles=ax.lines[-4:], 38                      labels=['{}'.format(df_temp.iloc[i,0]) for i in range(len(df_temp))])39        g.set(ylim=(0, 1.05), xlim=(0, recall_cutoff))40        g.set_titles(row_template = '{row_name}', col_template = 'PR-Curve (IOU>0.5)')41        42    return g43    44    45if __name__ == '__main__':46    47    # INITIALIZE DATA SCORES48    data1 = pd.read_excel(input_file1, sheet_name=None)49    list_models = [model for model in list(data1) if 'Model' in model]50   51    # APPEND DATAFRAMES OF EACH MODEL52    df_plot = pd.DataFrame()53    for model in list_models:54        df_model = data1[model].iloc[:, :-1]55        df_pivot = df_model.melt(id_vars=['Threshold'], var_name='Metrics', value_name='Score')56        df_pivot['Model'] = model.lstrip('Model_')57        df_pivot = df_pivot[[df_pivot.columns[-1], *df_pivot.columns[:-1]]]58        df_plot = df_plot.append(df_pivot, ignore_index=True)59    # CREATE METRICS PLOT60    plot_metrics = create_metrics_plot(df_plot)61    plot_metrics.savefig(current_folder + 'Plot_metrics.png')62    # INITIALIZE DATA PR DATA63    data2 = pd.read_excel(current_folder + input_file2, sheet_name=None)64    # APPEND DATAFRAMES OF EACH MODEL65    list_output = {}66    df_F1_model = pd.DataFrame(columns={'Model', 'Threshold', 'F1'})67    list_models2 = [model for model in list(data2)]68    for model in list_models2:69        df_model = data2[model]70        71        df_model['F1'] = 2*df_model['Recall']*df_model['Precision']/(df_model['Precision'] + df_model['Recall']) 72        df_model['F1'] = df_model['F1'].fillna(0)73        df_model['Model'] = model.split('Model_')[-1]74        df_agg = df_model.groupby(['Model', 'Threshold']).agg({'F1': lambda x: np.max(x)}).reset_index()...

test_model_performance.py

Source:test_model_performance.py

...17AIR_FILE = os.path.join(DATA_DIR, "air_passengers.csv")18YOS_FILE = os.path.join(DATA_DIR, "yosemite_temps.csv")19# Important to set seed for reproducibility20set_random_seed(42)21def create_metrics_plot(metrics):22    # Plotly params23    prediction_color = "#2d92ff"24    actual_color = "black"25    line_width = 226    marker_size = 427    xaxis_args = {"showline": True, "mirror": True, "linewidth": 1.5, "showgrid": False}28    yaxis_args = {"showline": True, "mirror": True, "linewidth": 1.5, "showgrid": False, "rangemode": "tozero"}29    layout_args = {30        "autosize": True,31        "template": "plotly_white",32        "margin": go.layout.Margin(l=0, r=10, b=0, t=30, pad=0),33        "font": dict(size=10),34        "title": dict(font=dict(size=10)),35        "width": 1000,36        "height": 200,37    }38    metric_cols = [col for col in metrics.columns if not ("_val" in col or col == "RegLoss")]39    fig = make_subplots(rows=1, cols=len(metric_cols), subplot_titles=metric_cols)40    for i, metric in enumerate(metric_cols):41        fig.add_trace(42            go.Scatter(43                y=metrics[metric],44                name=metric,45                mode="lines",46                line=dict(color=prediction_color, width=line_width),47                legendgroup=metric,48            ),49            row=1,50            col=i + 1,51        )52        if f"{metric}_val" in metrics.columns:53            fig.add_trace(54                go.Scatter(55                    y=metrics[f"{metric}_val"],56                    name=f"{metric}_val",57                    mode="lines",58                    line=dict(color=actual_color, width=line_width),59                    legendgroup=metric,60                ),61                row=1,62                col=i + 1,63            )64        if metric == "Loss":65            fig.add_trace(66                go.Scatter(67                    y=metrics["RegLoss"],68                    name="RegLoss",69                    mode="lines",70                    line=dict(color=actual_color, width=line_width),71                    legendgroup=metric,72                ),73                row=1,74                col=i + 1,75            )76    fig.update_xaxes(xaxis_args)77    fig.update_yaxes(yaxis_args)78    fig.update_layout(layout_args)79    return fig80def test_PeytonManning():81    df = pd.read_csv(PEYTON_FILE)82    m = NeuralProphet()83    df_train, df_test = m.split_df(df=df, freq="D", valid_p=0.2)84    metrics = m.fit(df_train, validation_df=df_test, freq="D")85    accuracy_metrics = metrics.to_dict("records")[-1]86    with open(os.path.join(DIR, "tests", "metrics", "PeytonManning.json"), "w") as outfile:87        json.dump(accuracy_metrics, outfile)88    create_metrics_plot(metrics).write_image(os.path.join(DIR, "tests", "metrics", "PeytonManning.svg"))89def test_YosemiteTemps():90    df = pd.read_csv(YOS_FILE)91    m = NeuralProphet(92        n_lags=24,93        n_forecasts=24,94        changepoints_range=0.95,95        n_changepoints=30,96        weekly_seasonality=False,97    )98    df_train, df_test = m.split_df(df=df, freq="5min", valid_p=0.2)99    metrics = m.fit(df_train, validation_df=df_test, freq="5min")100    accuracy_metrics = metrics.to_dict("records")[-1]101    with open(os.path.join(DIR, "tests", "metrics", "YosemiteTemps.json"), "w") as outfile:102        json.dump(accuracy_metrics, outfile)103    create_metrics_plot(metrics).write_image(os.path.join(DIR, "tests", "metrics", "YosemiteTemps.svg"))104def test_AirPassengers():105    df = pd.read_csv(AIR_FILE)106    m = NeuralProphet(seasonality_mode="multiplicative")107    df_train, df_test = m.split_df(df=df, freq="MS", valid_p=0.2)108    metrics = m.fit(df_train, validation_df=df_test, freq="MS")109    accuracy_metrics = metrics.to_dict("records")[-1]110    with open(os.path.join(DIR, "tests", "metrics", "AirPassengers.json"), "w") as outfile:111        json.dump(accuracy_metrics, outfile)...

run_process_metrics.py

Source:run_process_metrics.py

...24    png_filename = f"{filename_without_extension}.png"25    test_metrics = load_test_metrics_json(filename=json_filename)26    compare_test_metrics(error_on_diff=False, error_on_miss=False, test_metrics=test_metrics)27    metrics_dict = compute_metrics(test_metrics=test_metrics, interactive=False)28    create_metrics_plot(metrics_dict, filename=png_filename)29    logging.debug("Wrote {0}".format(png_filename))30if __name__ == "__main__":...

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