Example #1
0
def training(path):
    model_data = g.model_data_manager.models[path]
    cached_data = model_data.get_metrics(50)

    context = {
        'precision_recall_curve':
        plots.precision_recall_curve(cached_data),
        'roc_curve':
        plots.roc_curve(cached_data),
        'score_distribution':
        plots.score_distribution(cached_data[0]),
        'absolute_score_distribution':
        plots.absolute_score_distribution(cached_data[0]),
        'marginal_precision_curve':
        plots.marginal_precision_curve(cached_data[0]),
        'threshold_graph':
        plots.thresholds_graph(cached_data[0]),
        'threshold_table':
        plots.thresholds_table(cached_data[0]),
        'auc':
        auc(cached_data[0]['fprs'], cached_data[0]['recalls']),
        'notes':
        model_data.get_notes(),
        'model_metadata':
        model_data.get_metadata(),
        'path':
        path,
    }
    return render_template("results.html", **context)
Example #2
0
def compare():
    models = request.args.getlist('model[]')
    cached_datas = []
    for path in models:
        model_data = g.model_data_manager.models[path]
        cached_datas.append(model_data.get_metrics(10))

    _, ax = plt.subplots(figsize=(12, 6))
    for name, cached_data in zip(models, cached_datas):
        prc = plots.precision_recall_curve(cached_data, ax=ax, label=name)

    _, ax = plt.subplots(figsize=(12, 6))
    for name, cached_data in zip(models, cached_datas):
        roc = plots.roc_curve(cached_data, ax=ax, label=name)

    context = {'precision_recall_curve': prc, 'roc_curve': roc}

    return render_template("compare.html", **context)
Example #3
0
def training(path):
    model_data = g.model_data_manager.models[path]
    cached_data = model_data.get_metrics(50)

    context = {
        'precision_recall_curve': plots.precision_recall_curve(cached_data),
        'roc_curve': plots.roc_curve(cached_data),
        'score_distribution': plots.score_distribution(cached_data[0]),
        'marginal_precision_curve': plots.marginal_precision_curve(cached_data[0]),
        'threshold_graph': plots.thresholds_graph(cached_data[0]),
        'threshold_table': plots.thresholds_table(cached_data[0]),

        'brier': plots.box_brier(cached_data),
        'auc': auc(cached_data[0]['fprs'], cached_data[0]['recalls']),
        'notes': model_data.get_notes(),
        'path': path,
    }
    return render_template("results.html", **context)
Example #4
0
def training(path):
    model_data = ModelData(g.file_system, path)
    cached_data = model_data.get_metrics(50)
    # print cached_data

    context = {
        'precision_recall_curve': plots.precision_recall_curve(cached_data),
        'roc_curve': plots.roc_curve(cached_data),
        'score_distribution': plots.score_distribution(cached_data[0]),
        'marginal_precision_curve':
        plots.marginal_precision_curve(cached_data[0]),
        'threshold_graph': plots.thresholds_graph(cached_data[0]),
        'threshold_table': plots.thresholds_table(cached_data[0]),
        'brier': plots.box_brier(cached_data),
        'auc': auc(cached_data[0]['fprs'], cached_data[0]['recalls']),
        'notes': ModelData(g.file_system, path).get_notes(),
        'path': path,
    }
    return render_template("results.html", **context)
Example #5
0
def training(path):
    model_data = ModelData(g.file_system, path)
    cached_data = model_data.get_metrics(50)
    # print cached_data

    context = {
        # 'precision_recall_curve': plots.precision_recall_curve(cached_data),
        'support_precision_curve': plots.support_precision_curve(cached_data),
        'roc_curve': plots.roc_curve(cached_data),
        'score_distribution': plots.score_distribution(cached_data[0]),
        'marginal_precision_curve': plots.marginal_precision_curve(cached_data[0]),
        'threshold_graph': plots.thresholds_graph(cached_data[0]),
        'threshold_table': plots.thresholds_table(cached_data[0]),

        'brier': plots.box_brier(cached_data),
        'auc': auc(cached_data[0]['fprs'], cached_data[0]['recalls']),
        'notes': ModelData(g.file_system, path).get_notes(),
        'path': path,
    }
    return render_template("results.html", **context)
Example #6
0
def compare():
    models = request.args.getlist('model[]')
    cached_datas = []
    for path in models:
        model_data = ModelData(g.file_system, path)
        cached_datas.append(model_data.get_metrics(10))

    _, ax = plt.subplots(figsize=(12, 6))
    for name, cached_data in zip(models, cached_datas):
        prc = plots.support_precision_curve(cached_data, ax=ax, label=name)

    _, ax = plt.subplots(figsize=(12, 6))
    for name, cached_data in zip(models, cached_datas):
        roc = plots.roc_curve(cached_data, ax=ax, label=name)

    context = {
        'support_precision_curve': prc,
        'roc_curve': roc}

    return render_template("compare.html", **context)
Example #7
0
def compare():
    models = request.args.getlist('model[]')
    cached_datas = []
    for path in models:
        model_data = g.model_data_manager.models[path]
        cached_datas.append(model_data.get_metrics(10))

    _, ax = plt.subplots(figsize=(12, 6))
    for name, cached_data in zip(models, cached_datas):
        prc = plots.precision_recall_curve(cached_data, ax=ax, label=name)

    _, ax = plt.subplots(figsize=(12, 6))
    for name, cached_data in zip(models, cached_datas):
        roc = plots.roc_curve(cached_data, ax=ax, label=name)

    context = {
        'precision_recall_curve': prc,
        'roc_curve': roc}

    return render_template("compare.html", **context)