Predictions download formats: Image metric learning

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:

• embeddings
  • A 2-dimensional NumPy array that contains image embeddings. The shape of the array is (n, m), where n represents the number of observations, while m represents the {embedding_size} where {embedding_size} refers to the selected embedding size value used during the experiment. Images with nearby embedding vectors are predicted to have similar content
    Note

    You can define the {embedding_size} under the Architecture settings section when building an image metric learning experiment.

• cosine_similarities
  • A 2-dimensional NumPy array that contains cosine similarities between validation/test images. The shape of the array is as follows: number_of_observations x {top_k_similar} where {top_k_similar} refers to the selected Top K Similar value used during the experiment
• similar_images
  • A 2-dimensional NumPy array that contains indices of similar validation/test images. The shape of the array is as follows: number_of_observations x {top_k_similar} where {top_k_similar} refers to the selected Top K Similar value used during the experiment
• [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 metric learning experiment.

`.csv` file columns

The csv file contains the following columns:

• All the N columns found in the train dataframe
• Three columns name top_{k}_similar_image, where k can represent a number between 1 to 3, which contains the top k name of an image similar to the input image
• Three columns name top_{k}_cosine_similarity, where k can represent a number between 1 to 3, which contains the cosine similarity value between the input image and the top_{k}_similar_image

Note

• The i-th sample of each output's dictionary item matches the i-th row of the dataframe.
• 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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