Predictions download formats: Image and text classification
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:
validation_predictions.csv
: This is a structured dataframe in CSV format, presenting the final predictions for the provided validation dataframe.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:
test_predictions.csv
: This is another structured dataframe in CSV format, displaying the final predictions for the provided test dataframe.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:
test_predictions.csv
: This is a structured dataframe in CSV format, showing the final predictions for the provided test dataframe.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
- `.csv` file columns
The Pickle file, contains the following keys:
- predictions
- A 2-dimensional NumPy array that contains class probabilities. The shape of the array is (n, m), where n represents the number of observations, while m represents the number of classes
- labels
- A 1-dimensional NumPy array that contains label names
- [image_column]
- A 1-dimensional NumPy array that contains input image names. The name of the key is
[image_column]
whereimage_column
refers to the name of the image column in the train dataframeNoteYou can define the
[image_column]
under the Dataset settings section when building an image and text classification experiment.
- A 1-dimensional NumPy array that contains input image names. The name of the key is
- [text_column]
- A 1-dimensional NumPy array that contains input texts. The name of the key is
[text_column]
wheretext_column
refers to the name of the text column in the train dataframeNoteYou can define the
[text_column]
under the Dataset settings section when building an image and text classification experiment.
- A 1-dimensional NumPy array that contains input texts. The name of the key is
The csv file contains the following columns:
- All the N columns in the train dataframe
- N columns name
pred_{label_column_name}
containing probabilities for the label column,label_column_name
refers to the label column found in the train dataframe
- For multi-label and multi-class image and text classification experiments, more than one
pred_{label_column_name}
column is in the csv referring to the predicted probability for each of the label columns from the train dataframe - You can define the
label_column_name
(s) under the Dataset settings section when building an image and text classification experiment
- 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.pkl
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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