Merge pull request #385 from chemprop/readme-updates

Readme updates

README.md

@@ -26,7 +26,6 @@ Please see [aicures.mit.edu](https://aicures.mit.edu) and the associated [data G
   * [Docker](#docker)
 - [Web Interface](#web-interface)
 - [Within Python](#within-python)
-- [Reproducibility](#reproducibility)
 - [Data](#data)
 - [Training](#training)
   * [Train/Validation/Test Splits](#trainvalidationtest-splits)
@@ -74,7 +73,9 @@ Chemprop can either be installed from PyPi via pip or from source (i.e., directl
 
 Both options require conda, so first install Miniconda from [https://conda.io/miniconda.html](https://conda.io/miniconda.html).
 
-Then proceed to either option below to complete the installation. Note that on machines with GPUs, you may need to manually install a GPU-enabled version of PyTorch by following the instructions [here](https://pytorch.org/get-started/locally/).
+Then proceed to either option below to complete the installation. If installing the environment with conda seems to be taking too long, you can also try running `conda install -c conda-forge mamba` and then replacing `conda` with `mamba` in each of the steps below.
+
+**Note for machines with GPUs:** You may need to manually install a GPU-enabled version of PyTorch by following the instructions [here](https://pytorch.org/get-started/locally/). If you're encountering issues with Chemprop not using a GPU on your system after following the instructions below, check which version of PyTorch you have installed in your environment using `conda list | grep torch` or similar. If the PyTorch line includes `cpu`, please uninstall it using `conda remove pytorch` and reinstall a GPU-enabled version using the instructions at the link above.
 
 ### Option 1: Installing from PyPi
 
@@ -394,11 +395,14 @@ The uncertainty of predictions made in Chemprop can be estimated by several diff
 
 Uncertainty predictions may be calibrated to improve their performance on new predictions. Calibration methods are selected using `--calibration_method <method>`, options provided below. An additional dataset to use in calibration is provided through `--calibration_path <path>`, along with necessary features like `--calibration_features_path <path>`. As with the data used in training, calibration data for multitask models are allowed to have gaps and missing targets in the data.
 
-**Regression** Calibrated regression outputs can be in the form of a standard deviation or an interval, as specified with the argument `--regression_calibrator_metric <"stdev" or "interval">`. The interval can be set using `--calibration_interval_percentile <float>` in the range (1,100).
+**Regression** 
+
+Calibrated regression outputs can be in the form of a standard deviation or an interval, as specified with the argument `--regression_calibrator_metric <"stdev" or "interval">`. The interval can be set using `--calibration_interval_percentile <float>` in the range (1,100).
 * `zscaling` Assumes that errors are normally distributed according to the estimated variance for each prediction. Applies a constant multiple to all stdev or interval outputs in order to minimize the negative log likelihood for the normal distributions. (https://arxiv.org/abs/1905.11659)
 * `tscaling` Similar to zscaling. Assumes that the errors are normally distributed, but accounts for the ensemble size and uncertainty in the sample variance by using a sample-size reduced t-distribution in the negative log likelihood. Works best when errors are mostly due to variability between model instances and not dataset noise or model bias.
 * `zelikman_interval` Assumes that the error distribution is the same for each prediction but scaled by the uncalibrated standard deviation for each. Multiplies the uncalibrated standard deviation by a factor necessary to cover the specified interval of the calibration set. Does not assume a Gaussian distribution. Intended for use with intervals but can return a stdev as well. (https://arxiv.org/abs/2005.12496)
 * `mve_weighting` For use with ensembles of models trained with mve or evidential loss function. Uses a weighted average of the predicted variances to achieve a minimum negative log likelihood of predictions. (https://doi.org/10.1186/s13321-021-00551-x)
+
 **Classification**
 * `platt` Uses a linear scaling before the sigmoid function in prediction to minimize the negative log likelihood of the predictions. If the model checkpoint was generated after Chemprop v1.5.0, then a Bayesian correction is applied to account for the class balance in the training set during prediction. Implemented for classification but not multiclass datasets. (https://arxiv.org/abs/1706.04599)
 * `isotonic` Fits an isotonic regression model to the predictions. Prediction outputs are transformed using a stepped histogram-style to match the empirical probability observed in the calibration data. Number and size of the histogram bins are procedurally decided. Histogram bins are wider in the regions of the model output that are less reliable in ordering confidence. Implemented for both classification and multiclass datasets. (https://arxiv.org/abs/1706.04599)