Similarity ratio balanced top n tests

Python

from typing import List

import numpy as np
import pandas as pd
import plotly.express as px
from plotly.subplots import make_subplots
from sklearn.metrics import roc_curve, roc_auc_score

from prompt_playground.actionclip import images_features_df, VARIATION_NAMES
from prompt_playground.actionclip_similarities import (
    SimilarityParams,
    clips_texts_similarities,
    TopClassificationMethod,
)

Created a temporary directory at /var/folders/yc/slfs2kzs1d72wd8b20xw0nf80000gn/T/tmpf7ms79es
Writing /var/folders/yc/slfs2kzs1d72wd8b20xw0nf80000gn/T/tmpf7ms79es/_remote_module_non_scriptable.py

Python

def infer(TEXTS: List[str], TEXT_CLASSIFICATIONS: List[bool], VARIATION: str):
    similarities = clips_texts_similarities(
        SimilarityParams(
            texts=TEXTS,
            classifications=TEXT_CLASSIFICATIONS,
            model_variation=VARIATION,
            text_classification_method=TopClassificationMethod.any,
            texts_to_subtract=[],
            apply_softmax=False,
        )
    ).similarities

    alarms_series = images_features_df(VARIATION)["Alarm"]

    df = (
        pd.DataFrame(
            [
                dict(clip=clip, y_true=alarms_series[clip], **v.dict())
                for clip, l in similarities.items()
                for v in l
            ]
        )
        .rename(columns={"classification": "y_predict"})
        .sort_values(["clip", "y_true", "y_predict", "text"])
        .reset_index(drop=True)
    )

    unique_y_true = df["y_true"].unique()

    fig = make_subplots(
        rows=1,
        cols=4,
        column_widths=[0.3, 0.2, 0.3, 0.2],
        shared_yaxes=True,
        y_title="similarity",
        subplot_titles=[
            f"y_true={y_true}" for y_true in unique_y_true for _ in range(2)
        ],
    )

    # group by
    #  1. y_true (facet)
    #  2. y_predict / text class (color)
    for i, y_true in enumerate(unique_y_true):
        facet_df = df[df["y_true"] == y_true]

        for y_predict, class_color in zip(
            sorted(facet_df["y_predict"].unique()), ["CornflowerBlue", "Tomato"]
        ):
            facet_color_df = facet_df[facet_df["y_predict"] == y_predict]

            violin_side = "positive" if y_predict else "negative"

            fig.add_scatter(
                x=facet_color_df["text"],
                y=facet_color_df["similarity"],
                marker=dict(color=class_color, size=3),
                hovertext=facet_color_df["clip"],
                mode="markers",
                name=f"y_predict={str(y_predict)}",
                legendgroup=f"y_true={str(y_true)}",
                legendgrouptitle=dict(text=f"y_true={str(y_true)}"),
                row=1,
                col=i * 2 + 1,
            )
            fig.update_layout(**{f"xaxis{i*2+1}": dict(title="text")})
            fig.add_violin(
                x=np.repeat(str(y_true), len(facet_color_df)),
                y=facet_color_df["similarity"],
                box=dict(visible=True),
                scalegroup=str(y_true),
                scalemode="count",
                width=1,
                meanline=dict(visible=True),
                side=violin_side,
                marker=dict(color=class_color),
                showlegend=False,
                row=1,
                col=i * 2 + 2,
            )

    fig.update_layout(height=900, violingap=0, violinmode="overlay")
    fig.show()

    groupby_classification = df.groupby(["clip", "y_predict"])["similarity"]
    weighted_similarity = groupby_classification.sum() / groupby_classification.count()

    ratio = weighted_similarity.groupby(level="clip").aggregate(
        lambda s: s.loc[:, True] / s.loc[:, False]
    )

    ratio_df = ratio.to_frame("ratio")
    ratio_df["y_true"] = alarms_series.loc[ratio_df.index]

    fig = px.scatter(
        ratio_df.sort_values(["y_true", "ratio"]),
        y="ratio",
        color="y_true",
        render_mode="line",
        marginal_y="violin",
        height=900,
    )
    fig.show()

    fpr, tpr, thresholds = roc_curve(ratio_df["y_true"], ratio_df["ratio"])
    auc_score = roc_auc_score(ratio_df["y_true"], ratio_df["ratio"])

    roc_df = pd.DataFrame(
        {
            "False Positive Rate": fpr,
            "True Positive Rate": tpr,
        },
        columns=pd.Index(["False Positive Rate", "True Positive Rate"], name="Rate"),
        index=pd.Index(thresholds, name="Thresholds"),
    )
    fig = px.line(
        roc_df,
        x="False Positive Rate",
        y="True Positive Rate",
        title=f"{VARIATION} - AUC: {auc_score:.5f}",
        color_discrete_sequence=["orange"],
        range_x=[0, 1],
        range_y=[0, 1],
        width=600,
        height=450,
    ).add_shape(type="line", line=dict(dash="dash"), x0=0, x1=1, y0=0, y1=1)

    fig.show()

Python

TEXTS_TRUE = [
    "human",
    "a video of a human approaching a fence",
    "human going through a fence",
    "human cutting a fence",
]
TEXTS_FALSE = [
    "birds flying",
    "plastic bag laying on the floor",
    "rabbits",
    "insects",
    "foxes",
]
# assert len(TEXTS_TRUE) == len(TEXTS_FALSE)
TEXTS = TEXTS_TRUE + TEXTS_FALSE
TEXT_CLASSIFICATIONS = [True] * len(TEXTS_TRUE) + [False] * len(TEXTS_FALSE)


infer(TEXTS, TEXT_CLASSIFICATIONS, VARIATION_NAMES[1])

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