Predictions download formats: Image object detection

Overview

When you download predictions in H2O Hydrogen Torch, which comes in a zip file, the format and content of the file first depends on the problem type of the predictions, and then it depends on how you generate them. On the point of "how you generate them," there are two scenarios.

• Scenario 1: Predictions from a completed experiment

  Predictions downloaded from a completed experiment on the View experiments card are packaged in a zip file. This zip file contains the following files:

  1. validation_predictions.csv: This is a structured dataframe in CSV format, presenting the final predictions for the provided validation dataframe.

  2. validation_raw_predictions.pkl: This is a Pickle file, which is essentially a pickled Python dictionary. It contains raw predictions for the provided validation dataframe.

     If the experiment included a test dataframe, H2O Hydrogen Torch also includes two additional files in the same zip file:

  3. test_predictions.csv: This is another structured dataframe in CSV format, displaying the final predictions for the provided test dataframe.

  4. test_raw_predictions.pkl: Similar to the validation set, this is a Pickle file with raw predictions for the provided test dataframe.

• Scenario 2: Predictions generated by scoring on new data

  Predictions generated by scoring on new data through the H2O Hydrogen Torch UI (on the Predict data card) are downloaded in a zip file. This zip file includes the following files:

  1. test_predictions.csv: This is a structured dataframe in CSV format, showing the final predictions for the provided test dataframe.
  2. test_raw_predictions.pkl: This is a Pickle file, a pickled Python dictionary containing raw predictions for the provided test dataframe.

Formats

`.pkl` file keys

The Pickle file, contains the following keys:

• boxes
  • A 3-dimensional NumPy array that contains predicted bounding boxes. The shape of the array is as follows: The number_of_observations x number_of_bounding_boxes x 4
    Note

    The number_of_bounding_boxes is limited to 100 most confident boxes, all in the format of: (x_min, y_min, x_max, y_max).

• confidences
  • A 2-dimensional NumPy array that contains bounding boxes confidences (from 0 to 1). The shape of the array is (n, m), where n represents the number of observations, while m represents the number of bounding boxes
• classes
  • A 2-dimensional NumPy array that contains class names of bounding boxes. The shape of the array is (n, m), where n represents the number of observations while m represents the number of bounding boxes
• [image_column]
  • A 1-dimensional NumPy array that contains input image names. The name of the key is [image_column] where image_column refers to the name of the image column in the train dataframe
    Note

    You can define the [image_column] under the Dataset settings section when building an image object detection experiment.

`.csv` file columns

The csv file contains the following columns:

• A column named {image_column_name} where image_column_name refers to the image column name in the train dataframe
  Note

  You can define the image_column_name under the Dataset settings section when building an image object detection experiment.

• A column named x_min containing the minimum x coordinates for the bounding boxes
• A column named y_min containing the minimum y coordinates for the bounding boxes
• A column named x_max containing the maximum x coordinates for the bounding boxes
• A column named y_max containing the maximum y coordinates for the bounding boxes
• A column named confidence containing the confidence scores of all the corresponding bounding boxes, only bounding boxes with a confidence score larger than the probability_threshold are considered
  Note

  You can define the {probability_threshold} under the Validation settings section when building an image object detection experiment.

• A column named {class_name_column} containing the class names of all the corresponding bounding boxes, where class_name_column refers to the name of a column in the train dataframe referring to the class names

Note

To learn how to open the csv and Pickle files, see Open CSV and Pickle files with Python.

Open CSV and Pickle files with Python

Using Python, a csv or Pickle file containing predictions can be open as follows:

import pickle
import pandas as pd

df = pd.read_csv('text_classification/validation_predictions.csv')

with open('text_classification/validation_raw_predictions.pkl', 'rb') as f:
    out = pickle.load(f)

print(out.keys())

dict_keys(['predictions', 'comment_text', 'labels'])

print(df.head(1))

id	comment_text	label_toxic	label_severe_toxic	label_obscene	label_threat	label_insult	label_identity_hate	fold	pred_label_toxic	pred_label_severe_toxic	pred_label_obscene	pred_label_threat	pred_label_insult	pred_label_identity_hate
000103f0d9cfb60f	D'aww! He matches this background colour I'm s...	0	0	0	0	0	0	0	0.00041	0.000168	0.000328	0.000142	0.000247	0.000155

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